Smart Building–Grid Interaction in Urban Energy Transitions: A Taxonomy of Key Performance Indicators and Enabling Technologies

Abstract

Urban energy systems are expected to undergo a rapid transition towards smart, sustainable, and resilient infrastructures. Within this transformation, the interaction between smart buildings and energy grids plays a critical role in shaping future urban energy solutions. Smart building–grid interaction strategies facilitate the bidirectional energy flow between buildings and urban energy systems and support the integration of renewable energy sources (RESs) into cities’ energy systems through advanced control systems, sensing technologies, and digital infrastructures. However, the adoption of these solutions remains complex due to fragmented key performance indicators (KPIs) and the diversity of enabling technologies, and it requires accurate performance-driven design and operation. Despite recent advancements, the management and evaluation of the interaction of smart buildings and urban energy systems remain challenging due to overlapping and fragmented KPIs as well as the complexity of enabling technologies. Therefore, this study aims to review the recently published research works and provide a holistic taxonomy of KPIs and enabling technologies for such interplay between smart buildings and urban energy systems to achieve the goal of sustainable energy transition in cities. The study identifies and categorizes several existing KPIs across sustainability dimensions, including technical, environmental, economic, and social, covering the KPIs to measure the performance of smart building–urban energy systems from a sustainability-aware lens, offering an integrative framework for assessing urban energy resilience and efficiency. Additionally, the study contributes to classifying the enabling technologies for smart building and urban energy system interaction and discusses the interdependencies among such technology clusters. The findings contribute to ongoing urban energy transitions by promoting systemic approaches to planning, performance evaluation, and decision-making for sustainable and equitable urban energy futures. This contributes to the sustainability of the building and energy sectors at the urban scale by promoting and helping multi-dimensional performance assessment and informed decision-making.

1. Introduction

The overreliance on fossil fuels to supply the increasing energy needs of societies, including the building sector, has led to an environmental crisis over the decades [1]. Reportedly, the building sector alone is responsible for around 30–40% of the final energy consumption and greenhouse gas (GHG) emissions worldwide [2,3]. Furthermore, the power generation sector is expected to expand to meet the escalating energy needs, and its associated environmental emissions have shown an increasing trend globally [4]. The global shift toward decarbonized and resilient urban energy systems has positioned buildings as pivotal agents in balancing electricity demand and supply [5]. In this context, smart buildings are identified as key enabler solutions due to their capabilities of adapting to external climate and grid conditions, integrating RESs, and interacting with occupants in real-time by enhancing automation, adaptability, interactivity, and energy efficiency through the integration of smart technologies such as Building Energy Management Systems (BEMS), Internet of Things (IoT), and advanced control systems [6]. Furthermore, the existing power grids are expected to be transformed from centralized to smart distributed generation systems within an urban energy transition framework, allowing renewable energy source (RES) integration [7], facilitating two-way communication, enhancing demand response management and energy flexibility through adaptive and advanced control systems, and bidirectional energy flows [8].

In such a context, the building sector and its interaction with the grid systems as an interactive player, crucial to facilitating a higher penetration rate of RES and energy flexibility, becomes significantly important for successful and holistic urban energy transitions towards sustainable cities and societies, and the concept of grid-interactive smart buildings has emerged as a critical area of innovation [9]. Smart building–grid interaction is not only beneficial but also an essential strategy, particularly when buildings are both consumers and producers of energy (prosumers) and contribute to balancing the energy supply and demand, leading to higher grid stability and enhanced energy efficiency at both building and grid infrastructure levels [10]. Without such coordinated interaction, technical challenges such as load mismatches, grid overloading, and inefficient use of RESs in the building might become intensified.

Several research papers contributed to enlightening the necessity of such interaction between smart buildings and smart grids in the literature. For instance, a study emphasizes the fact that without active demand response management in buildings, grid stability cannot be achieved in the presence of RES penetration [11]. Furthermore, other studies expand the idea of smart grid into Smart Energy Systems (SES), aiming at integrating electricity, heat, and gas networks with storage and control systems to optimize the whole energy ecosystem [12]. Moreover, another concept named flexumers (flexible consumers/producers) is explained in a few studies aiming at optimizing the dynamic energy behavior and real-time service to the grid [13].

Further studies are found in the literature aiming at the evaluation and categorization of the smart buildings’ KPIs and technologies. For instance, Al Dakheel et al. [14] presented a clear framework of smart building features and key performance indicators through a critical review. In another study, Zhang et al. [15] offered an analysis of grid interaction indicators and the technical necessity of building–grid coordination in zero-energy buildings. Tang et al. [16] discussed flexibility types and the role of building systems in grid response. Li et al. [17] categorized several data-driven KPIs for energy flexibility across different dimensions, such as complexity, data needs, stockholder relevance, and application phase. Farulla et al. [18] highlighted the crucial role of the building as an active participant in managing the variability of RESs and system-level flexibility and classified KPIs based on control strategies and flexibility dimensions. Kifor et al. [12] presented a smart energy KPI framework focused on technical, operational, and educational metrics for a holistic system evaluation. In summary, the interaction between smart buildings and smart grids offers pivotal strategies to achieve decarbonized and resilient energy systems. The associated challenges with the measurement and implementation of sustainable solutions have been known as a critical obstacle to be tackled to fully unlock the transition towards sustainability [19]. Therefore, the successful application of smart building–grid interaction as a sustainable solution requires a comprehensive understanding of well-defined KPIs as well as enabling technologies to measure the progress and overcome the current challenges for unlocking the full potential of smart building–grid interconnected systems. Therefore, providing a robust framework for key performance indicators is critical to have holistic evaluations of the progress towards effective and efficient smart building grid interactions. These KPIs enable both quantitative and qualitative performance evaluations of building–grid interaction and support real-time control, predictive maintenance, and future energy planning.

Despite the promising advancement, the effective management and evaluation of smart building–grid interaction remain challenging due to several technical and non-technical reasons, including the fragmented key performance indicators (KPIs) for the assessment and the variety and complexity of the enabling technologies addressed in the literature. This research aims to provide a holistic image of well-adopted KPIs and enabling technologies implemented in the recent literature for smart building–grid interaction. Such a framework will be presented as a comprehensive taxonomy for KPIs according to the sustainability dimension, including technical, environmental, economic, and social aspects, helping designers and policymakers to assess the advances in this field through a sustainability-aware lens. Furthermore, current technologies implemented in different studies are reviewed and clustered into various categories according to the system application layer and functional objectives that help clarify and comprehend the interplay and interdependencies among enabling technologies as well as performance criteria.

The novelty of this study lies in extending prior KPI and technology classifications by mapping cross-dimensional interdependencies between sustainability indicators and technological clusters. The presented taxonomy in this study integrates the four-dimensional KPIs and application-based technological clusters into an interactive framework that can guide decision-makers in adopting suitable KPIs and promoting enabling technologies for the flexible and grid-responsive design of smart buildings and smart grids for sustainable urban energy transitions. Such a review-based taxonomy helps the designers, researchers, and policymakers to better implement and analyze smart building–grid systems and development programs for urban energy transition with a holistic approach covering all sustainability dimensions for existing technologies and future energy system scenarios.

2. Materials and Methods

To achieve the goal of the present study, for providing a clear framework and taxonomy of existing KPIs and enabling technologies for smart building–grid interaction, the recently published scientific articles in English between 2020 and 2025 indexed in the Scopus database are explored. For this purpose, initially, articles addressing keywords including “smart building” AND “grid” AND “interaction” and “key performance indicator” OR “KPI” AND “technology” OR “technologies” are filtered. In addition to the search query strings, and to ensure the inclusion of the most relevant studies, only publications that simultaneously focused on both building and grid-level scales are included in this review. By applying the aforementioned filters, 27 research articles relevant to the goal and scope of this research were retrieved. These articles were initially examined by reviewing the total keywords addressed to evaluate and generate a general map of covered topics and their interplays in the literature. Following the first round of the initial review, the KPIs and enabling technologies employed in all retrieved articles were rationally explored to create a comprehensive image of the latest and most frequent KPIs and technologies implemented in the literature.

Later on, the retrieved KPIs from the collected articles were carefully reviewed, discussed, and further categorized to create a comprehensive taxonomy. KPIs are categorized according to main rules: first, the affinity of objectives of each KPI and their level of application; and second, the inclusion of main sustainability objectives adopted in the built environment, as well as smart building–grid systems. Likewise, the enabling technologies for smart building–grid interaction addressed in the reviewed articles are classified according to their objectives and functions as well as the implementation system layer. Each KPI and enabling technology in different categories will then be analyzed and discussed concerning smart building–grid interaction to provide a reference for their further use and application in future research works.

3. Results

In this section, a bibliometric co-occurrence analysis is conducted using VOSviewer software v.1.6.20 on Scopus-indexed publications based on the filters introduced in the methodology section. The visualization of the results illustrates the conceptual landscape of this interdisciplinary field of study and highlights the dominant research directions, emerging trends, and interrelated topics. The two keyword co-occurrence maps provided in this section represent the overall picture of the research literature in this field. The first map provides a comprehensive view of the topic clusters, and the second one emphasizes the central topics and their interconnections.

The analysis reveals smart building as one of the critical nodes in the conceptual network derived from the literature, underscoring its role as a unifying concept across multiple research themes related to building–grid interaction. As presented in Figure 1, it is closely associated with further concepts such as energy flexibility, demand-side management, renewable energy, and energy efficiency, as well as key performance indicators, storage systems, and the grid. These keywords collectively indicate the growing research work focusing on transforming buildings from passive energy consumers to active, dynamic, grid-responsive integrated systems.

Figure 1. The co-occurrence map of explored keywords in smart building–grid interaction studies in the literature between 2020 and 2025.

Moreover, several distinct clusters are discovered in the literature. A major cluster centered on energy performance and efficiency reaffirms the continuing interest in the evaluation and improvement of the performance and sustainability of building and grid systems. This cluster often overlaps with keywords related to benchmarking, green buildings, and life cycle assessment, revealing continuous concerns about environmental impacts. Another important visible cluster focuses on renewable energy integration, including terms such as renewable energy resources, energy utilization, energy storage, and power grids. This cluster, however, is in close relationship with the previous cluster and mainly focuses on decentralized energy generation and storage strategies, such as technologies for photovoltaics and battery systems.

Regarding the grid interaction side, a large and increasingly important cluster revolves around demand response management, smart grids, power generation, photovoltaic systems, and energy transition, suggesting that the buildings are increasingly being studied as part of broader interactive energy networks capable of responding to grid conditions and contributing to the grid stability. However, the structure of the graphs indicates that grid-oriented research remains, to some extent, scattered, suggesting the need for stronger integration with building-side design and control studies.

Another emerging theme centers on smartness in the building sector, including keywords such as smart building, smart home, intelligent building, which—through Building Energy Management Systems (BEMS)—signify the transition toward automation, control, and data-driven optimization. Furthermore, there are scattered but still significant keywords related to economic evaluation and performance metrics, including terms such as economic cost and investment that suggest an ongoing effort to assess the economic feasibility and effectiveness of smart energy interventions from both technical and economic perspectives.

The analysis of the co-occurrence of keywords resulted in a network with 59 unique terms that are interconnected with 565 co-occurrence links. These terms are organized into five groups, each reflecting a primary thematic direction in the literature as described earlier. The overall density of this keyword network is 0.33, showing the research field is well-integrated and suggests that a significant number of keywords are interconnected rather than isolated. This indicates that the literature exhibits a notable level of conceptual overlap with many studies that simultaneously address multiple research subjects. Furthermore, the network configuration illustrated in Figure 1 implies that the research field has evolved around strongly linked themes, showing an advanced body of research. The co-occurrence of keywords illustrated in Figure 1, such as energy flexibility, demand side management, greenhouse gas emission, life cycle assessment, renewable energies, energy storage systems, etc., is directly informing and applied for the development of the structured taxonomy for KPIs and enabling technologies in this study, as presented in the following sections.

Overall, the map created based on bibliometric data indicates a conceptual shift in the field from energy-efficient building design as a primary objective to a broader concept of integrating buildings with smart, decentralized energy systems interconnected with smart grids through various advanced technological solutions. The research themes in this regard are mainly focused on energy flexibility, interactivity, and smart technologies, making buildings key active elements of integrated smart building–grid configuration.

3.1. Taxonomy for Key Performance Indicators

The taxonomy of KPIs derived from the scientific literature on smart building–grid interaction, as presented in Table 1, provides a comprehensive and hierarchical classification of existing KPIs. This taxonomy covers multi-dimensional performance criteria for buildings and districts acting as interactive components within low-carbon energy infrastructures. The present taxonomy follows established sustainability frameworks, categorizing KPIs into four primary clusters: technical, environmental, economic, and social. The technical cluster is further structured into sub-clusters, including energy efficiency, energy flexibility, renewable integration, and grid interaction. This clustering approach reflects the functional characteristics and application objectives of each KPI within the context of smart building–grid interactions concerning the sustainability dimensions.

Table 1. The taxonomy of key performance indicators in smart building–grid interaction studies.

Cluster	Subcluster	KPIs	Reference
Technical	Energy Efficiency	Cover Factor; Capacity Factor; Load Factor; Energy Consumption; Load Cover Factor; Supply Cover Factor; Net Electricity; Performance Efficiency; Load Match Ratio; Weighted Matching Index; Energy Density; System Efficiency; Performance Ratio; Final Yield; Net Direct Energy Consumption; Energy Demand Reduction; Optimized Load Curves	[20,21,22,23,24,25,26,27,28,29,30,31,32,33]
	Energy Flexibility	Flexibility Capacity Index; Flexibility Index; Maximum Flexibility Power; Relative Flexibility Ratio; Flexibility Utilization Efficiency; Load Shifting Factor; Shiftable Flexible Load; Energy Flexibility Provided by Building; Peak Shaving/Load Shifting Behavior; Energy Flexibility via Load Shifting; Flexibility Signal Integral Over Time; Flexibility Signal Distribution; Peak Load Reduction; Energy Demand Shift Amount; Load Demand Reduction; Flexibility Activation Hours; Peak Shaving Indicator; Power Flexibility Potential; Battery Flexibility; Thermal System Flexibility; Scenario-Based Demand Response Capacity; Demand Shift Volumes	[27,30,32,34,35,36,37,38,39,40,41]
	Renewables Integration	Self-consumption Index; Self-sufficiency Index; Energy Autonomy; PV Utilization Ratio; Onsite Energy Fraction; Onsite Energy Matching; PV Self-consumption Ratio; Energy Self-sufficiency Ratio; Renewable Shifted Ratio; Renewable Energy Discharging Ratio; Renewable Penetration Ratio	[20,22,24,25,27,31,38,40,42,43,44]
	Grid Interaction	Grid Export Power Profile; Net Load Management; Cluster Load Flattening; PV Forecasted Power Output; Grid-Friendly Interaction Indicator; Peak Power Indicators; Grid Flexibility Factor; Grid Utilization Ratio; Peak-to-Average Ratio; Loss of Load Probability; Peak Power Generation; Peak Power Load; Connection Capacity Credit; Grid Robustness Factor; Grid Interaction Index; Valley Filling Indicator; Annual Grid Import/Export Power; Net Annual Energy Exchange; Grid Shifted Ratio; Grid Fluctuation Reduction (%)	[20,21,22,24,25,27,31,32,33,36,37,38,40,42,44]
Environmental	-	Carbon Dioxide Emissions; Emission Index; Annual Operation Equivalent Carbon Emissions; Net Carbon Emissions; CO2 Emissions Reduction; Non-Renewable Primary Energy; Primary Energy Savings	[23,24,25,27,31,36,39,42,43,45,46]
Economic	-	Total Cost; Total Electricity Cost; Annual Electricity Bill; Relative Net Present Value; Lifetime Net Present Value; Initial Investment Cost; Operating Cost; Payback Period; Life Cycle Cost; Flexibility Cost Benefits; Electricity Cost Reduction; Economic Efficiency Index; Levelized Cost of Energy; Electricity Purchase Profiles	[21,23,24,25,29,32,34,35,36,42,43,46,47]
Social	-	Smart Readiness Indicator Score; Smart Readiness Impact Scores; Functionality Levels for Building Automation and Control Systems; Forced Flexibility Factor; Delayed Flexibility Factor	[26,31]

The results of the bibliometric analysis of KPIs for smart building—grid interaction in urban energy transition among the reviewed papers reveal several important insights into understanding how different dimensions and performance criteria are implemented and prioritized across the literature. The four plots in Figure 2 illustrate not only the distribution of KPIs but also the research trends and research gaps in this field.

Figure 2. Summary and graphical presentation of review findings for distribution of KPIs coverage among reviewed papers.

A central finding across the three plots (a, b, and c) in Figure 2 is the significant dominance of the technical KPIs over the other clusters, such as economic, environmental, and social dimensions, with nearly 73% of KPIs identified in relation to the technical performance dimension. In contrast, the share of KPIs in economic, environmental, and social dimensions accounts for around 14.6%, 7.3%, and 5.2%, respectively. This imbalance reveals a clear research bias and gap in measuring the potential of smart building–grid interaction for enhancing economic, environmental, and social dimensions, while the technical dimension is the main focal point in existing research works. The fact that among the KPIs associated with the technical cluster, the majority of distinct KPIs fall in the subclusters such as energy flexibility, grid interaction, and energy efficiency, ranging from 16 to over 20 distinct KPIs, suggests that within the technical focus, the researchers are mainly concerned with designing flexible and grid-responsive building systems in urban energy infrastructures. The scatter plot in Figure 2d provides additional information by mapping the number of adopted KPIs against the number of papers implementing each cluster and subcluster of KPIs. The results in Figure 2d indicate that the KPI subcluster, including grid interaction, energy flexibility, and efficiency, is relatively mature, well-developed, and extensively implemented in empirical validation and experimentation, as located in the top-right section of the plot. In contrast, the social and environmental dimensions are less implemented in the reviewed studies, representing the lower number of adopted KPIs and research papers, while some clusters and subclusters, such as economic and renewable integration, are moderately implemented and adopted in the literature in terms of the number of distinct KPIs and studies.

These findings together reveal a research landscape in the field of smart building and grid interaction in urban energy transitions, where the technical aspects are the main research focus, while broader sustainability dimensions covering environmental, economic, and social aspects are less explored and evaluated and indicate that further research effort is required in expanding social KPIs, and economic evaluations and deepening environmental indicators are required to provide holistic evaluations of smart building–grid interaction for urban energy transitions schemes. Furthermore, despite the wide range of KPIs identified and proposed in this review, mainly the KPIs categorized in technical clusters are widely implemented in practical and real case studies, while the other KPIs, such as those identified in the social cluster, still remain mainly conceptual. Moreover, KPIs linked to the environmental and economic dimension require external data from the environmental and economic context that might increase the uncertainties of the results, while technical KPIs rely on the measurable and more robust and reliable inputs in each case study. However, this implies further required research to standardize methods to measure non-technical KPIs in future studies, since they are integral elements to design policies for sustainable urban energy transition roadmaps.

3.1.1. Cluster 1: Technical Key Performance Indicators

The first cluster, named technical, comprises the four previously introduced subclusters and emphasizes the pathways through which buildings participate in efficient and responsible energy consumption, energy generation, and exchange. The first subcluster, energy efficiency-related KPIs (e.g., load and cover factor, performance ratio, etc.), establishes the baseline for efficient energy performance criteria at both building and district levels, providing possibilities of creating benchmarks across various system levels and boundary systems. Energy flexibility-related indicators, as grouped in the second subcluster, quantify the capabilities of smart building/grid systems to manage demand response and integration of multiple energy systems flexibly, alongside the control and adjustment of load and generation profile, which plays a critical role in the effective and efficient use and management of accessible energy sources. Key performance indicators related to renewables integration, on the other hand, focus on measuring the penetration of renewable energy sources (e.g., solar, wind, etc.) in energy grids as well as the share of renewable energy sources in generation and final consumption at the building level using KPIs such as self-consumption, self-sufficiency, energy autonomy, as well as other related KPIs listed in Table 1.

Finally, grid interaction KPIs such as grid import/export power, net annual energy exchange, grid interaction index, grid robustness factor, etc., translate localized operational shifts into distribution system benefits and help identify the interplay between building as a prosumer system and the energy grid by quantifying the loads and energy exchanged at different resolution levels. Together, these subclusters and related KPIs provide a detailed map of technical layers of information available to designers, users, and policymakers aiming at the efficient integration of buildings as prosumer units into dynamic energy infrastructures.

3.1.2. Cluster 2: Environmental Key Performance Indicators

The second cluster, named environmental, focuses on carbon emissions and primary energy used in smart buildings integrated into smart grids. Two major groups of indicators, including emission-related and primary energy-related KPIs, as the two sub-clusters, provide essential environmental information for decarbonization planning according to life cycle assessment methods. KPIs in this cluster are critical measures to realize the bilateral impacts of smart buildings and grid systems on the environmental quality of the context within different scopes of assessment, including the building level, distribution grid, and power generation levels. These KPIs help designers and policymakers realize the impact of building–grid configuration on the environmental performance of the smart building–grid systems as a whole to achieve the decarbonization targets. Given the retrieved environmental KPIs, the reliance of the evaluation on only carbon emission in the literature and the lack of a full life cycle assessment approach incorporating various environmental impact indicators highlight the need for further investigation with a more comprehensive environmental assessment framework.

3.1.3. Cluster 3: Economic Key Performance Indicators

The economic cluster, including cost flow metrics, investment metrics, and value metrics as the main three sub-clusters, translates technical and environmental performance into financial outcomes. These KPIs are crucial metrics that help identify the investment opportunities in smart building–grid systems by providing quantified indicators allowing techno-economic assessments on alternative planning and development of alternative models and strategies at both the building and grid infrastructure scales. The information potentially derived from techno-economic analyses using these indicators enables building designers and policymakers to develop strategies with the highest economic viability and financial robustness.

3.1.4. Cluster 4: Social Key Performance Indicators

The KPIs in this cluster, named social, along with its sub-clusters, including automated system performance and behavioral metrics, measure both the maturity level of control system architectures and the real-world presentation of demand response signals. These KPIs expose the human–technology interfaces as a critical factor of realized savings and grid benefits. This cluster, therefore, emphasizes user acceptance and interaction as a design parameter, rather than bridging the performance gap between simulated and actual outcomes afterwards. As illustrated in Figure 2, social KPIs are the least explored indicators both in terms of the number of distinct KPIs and the number of studies in the literature. Although in the review, few social KPIs mainly linked with smart readiness indicators and flexibility factors are found among the reviewed papers, future studies should also integrate further social aspects, such as user acceptance rate, user participation rate, comfort, and behavioral flexibility, as decisive factors for grid responsiveness. Moreover, future studies should contribute to the advancement of measurable social KPIs, including the standardization of measuring and calculation methods.

3.2. Taxonomy for Enabling Technologies

The effective and successful integration of smart buildings into smart grids depends on the advancement of technological solutions that support various functions and potentials, such as flexibility, intelligence, and interoperability. The proposed taxonomy in this study (Table 2) suggests five main clusters, including energy infrastructure, sensing and measurement, generation, control and automation, energy storage, and data analytics, which together offer a systematic classification of technologies to enable smart building–grid interaction. Such a taxonomy not only provides a clear technological landscape but also facilitates defining research and development directions by clarifying the technological roles and interdependencies.

Table 2. The taxonomy of enabling technologies in smart building–grid interaction studies.

Cluster	Subcluster	Technology/Tools	References
Energy Infrastructures	Grid Interaction Facilities	Smart Grid Interface	[20,21,22,23,24,25,26,27,31,35,36,40,42,44]
		Grid Connection Systems
	Microgrid Facilities	Community Microgrid Systems
	Charging Facilities	EV Charging Interface
Sensing and Measurement	Energy Meters	Smart Meter	[28,30,40,41]
	Indoor Air Quality (IAQ) Sensors	Temperature Sensor
		Real-Time CO2 Sensor Data
	Environmental Sensors	Weather Station (Irradiance and Wind Sensors)
Generation, Control, and Automation	Energy Systems and Management	Photovoltaic (PV)	[20,21,22,23,24,25,26,27,28,29,31,32,33,34,35,36,37,38,39,42,43,44,48]
		Combined Cooling, Heating, and Power (CCHP)
		Electric Vehicles (EVs)
		Heat Pump
		Energy Management System (EMS)
		Smart Home Energy Management System (HEMS)
	Optimization Algorithms	Evolutionary and Genetic Algorithm
	Control Strategies	Rule-Based and Adaptive Predictive Control Systems
	Building Automation Systems	BACS (Building Automation and Control Systems)
		Smart HVAC Controls
Energy Storage	Battery Storage	Battery Energy Storage System (BESS)	[21,24,32,33,34,36,38,40,42,45,46,47]
	Thermal Storage	Thermal Energy Storage Tank
Data Analytics	Simulation, Learning, and Data Analytics Tools	TRNSYS; MATLAB/Custom Model Machine Learning Algorithms Multi-objective Evaluation Framework	[22,31,33,34,40,44]

The bibliometric analysis on smart technologies in the field of smart–grid interaction for urban energy transitions reveals notable patterns regarding the distribution of research attention across different technologies, summarized and illustrated in Figure 3. The share of the reviewed publication shown in Figure 3a demonstrates a heterogeneous distribution across different technologies categorized in 5 clusters and 13 subclusters in this study, and identifies generation, control, and automation as the main technological focus in the literature explored in 39% of the reviewed publications. This dominance highlights the centrality of the system-level management of the technologies in smart building–grid systems as a primary driver for urban energy transitions in a context where buildings evolve from passive energy consumers to active participants in urban energy systems.

Figure 3. The summary and graphical presentation of review findings for the distribution of enabling smart technologies among the reviewed papers.

The second most evaluated and dominant technology cluster is energy infrastructures, with around 26% share of the literature emphasizing on fundamental technologies such as grid and microgrid facilities for enabling smart building–grid as a promising technological approach in urban energy transitions. Furthermore, energy storage technologies, including thermal and electrical storage systems, are known as another promising technological solution, particularly in the presence of renewable energy sources in urban energy systems, and contribute to 18% of the literature in this field, ranked as the third most surveyed technology cluster in the literature. The smallest share of the literature is associated with data analytics and sensing and measurement clusters, contributing to only 9% and 8% of the literature as the least explored technological solution, although they have a critical role in real-time and effective smart building–grid interaction for urban energy transitions.

The scatter plot presented in Figure 3b reaffirms the findings, showing the cluster of generation, control, and automation as the most mature technological cluster, with the highest number of technologies and the highest number of studies adopting and evaluating this cluster of smart technologies, while the energy systems with the lowest variety of existing technologies and data analytics, the focus of the lowest number of studies, can be realized as the area for further research efforts in the future.

3.2.1. Cluster 1: Energy Infrastructures

The energy infrastructures cluster provides a physical and operational basis for smart building–grid interaction and encompasses three main subclusters, including grid interaction facilities, microgrid facilities, and charging facilities. The technologies under the first subcluster, such as smart grid interface and grid connection systems, provide the possibility of bidirectional communication and enable electricity exchange between the building and smart grids as critical and essential elements for real-time coordination, demand response programs, and integration of distributed energy sources. Microgrid facilities focus on microgrid systems enabling energy resiliency, particularly during grid stress periods and power outages. Meanwhile, charging facilities support the growing role of electric vehicles integrated into smart building–grid systems. The technologies in this cluster aim to support and ensure access and contribution to grid services in a secure, efficient, and interoperable way.

3.2.2. Cluster 2: Sensing and Measurement

Accurate and real-time data acquisition is a critical factor for predictive and responsive operation as a key ability of smart buildings. The sensing and measurement cluster comprises four subclusters, including energy meters, temperature sensors, environmental sensors, and occupancy sensors. Smart energy meters provide high-resolution data on electricity consumption and generation, enabling the whole system to be effectively interactive based on real-time data. Temperature sensors support real-time control of thermal comfort and Heating, Cooling, and Air Conditioning (HVAC) operation, while environmental sensors, including irradiance and wind measurement systems, enhance renewable energy generation forecasting. On the other hand, occupancy sensors, such as tools to measure indoor air quality and presence data, help improve demand-driven control strategies for heating, cooling, ventilation, and artificial lighting, and consequently, efficient management of energy systems while ensuring indoor environmental quality. These technologies together create the sensory layer of a smart building, providing real-time data required for adaptive control and optimization of smart building–grid interaction.

3.2.3. Cluster 3: Generation, Control, and Automation

The cluster named generation, control, and automation represents the intelligence required for enabling autonomous building functions and dynamic interaction with smart grids. This cluster encompasses four primary subclusters, including energy management systems, optimization algorithms, control strategies, and building automation systems. The primary objective of the energy management system is to integrate and optimize the operation of different energy systems, such as photovoltaics, heat and power generation systems, heat pumps, etc. Energy management systems aim to facilitate the coordination of on-site energy generation and consumption as well as energy storage in response to occupant needs, energy prices, and grid signals.

Control strategies—from simulation and rule-based models to advanced predictive models—determine the logic for the management of energy systems at both consumption and generation levels and their underplays in smart buildings and therefore have a crucial role in the efficiency of smart buildings. Optimization algorithms in this context play a pivotal role in solving complex problems such as cost optimization, emission reduction, etc., through finding optimal operation schemes. Finally, building automation systems ensures that high-level control strategies and decisions are effectively and successfully implemented at both device and integrated energy system levels. The technological solutions in this cluster translate system-level goals into operational actions, serving as an intermediary between the system intelligence and physical actions.

3.2.4. Cluster 4: Energy Storage

Energy storage technologies play a critical role in smart buildings coupled with renewable energy sources and are presented in this taxonomy by two sub-clusters, including battery storage and thermal storage. Battery Energy Storage Systems (BESS) provide rapid response flexibility, enabling peak shaving, load shifting, enhanced self-consumption of renewable energy resources, etc. Their integration into smart buildings is crucial for reducing grid dependency and improving the energy resiliency of buildings, as well as minimizing the stress and loads on the energy grids. Likewise, thermal energy storage can effectively reduce the peak demand and shift the heating and cooling load of the building and consequently decrease the operational energy costs of the buildings while maintaining the occupants’ comfort levels. Together, these technologies can enhance the controllability of energy systems and contribute to the demand-side management and flexibility in smart buildings.

3.2.5. Cluster 5: Data Analytics

The data analytics cluster provides computational and analytical capabilities necessary for the smart system’s functions, such as system intelligence, predictive modeling, decision-making, etc., as core characteristics of previous clustered technologies. This cluster includes three main sub-clusters named simulation, learning, and analytical tools, encompassing software platforms and algorithms. For instance, simulation tools are critical software for high-resolution simulation outputs of the building performance and control systems, which are critical for system design and validation, while machine learning (ML) algorithms play a substantial role in enabling automated fault and abnormality detection in system operation and enable adaptive control that evolves with environmental conditions and occupant behavior. Likewise, analytical tools support strategic energy planning, policy analysis, and system-level optimization. These technologies, clustered as data analytics, together provide the basis of capabilities of data-driven energy management, enabling buildings to become predictive and autonomous agents within the smart grid.

4. Discussion

This section discusses the cross-cluster integration and implications in the two proposed taxonomies for KPIs and smart enabling technologies and elaborates on how the clusters can be linked to each other for a holistic measurement and implementation of smart building–grid interaction for urban energy transition. Furthermore, the link between the two taxonomies is discussed to highlight the interconnectivity between the KPIs/ technologies’ dual taxonomy. In the KPI taxonomy introduced in the previous sections, it should be noted that while each cluster has a distinctive goal and target, the real value of the taxonomy is its capacity to highlight the synergies across different dimensions. For instance, enhancing energy flexibility (technical cluster) can reduce the CO₂ equivalent emission (environmental cluster) by aligning the consumption profile with renewable energy generation peaks of the occupants following effective schedule adjustment (social cluster), and if the additional automation investment remains economically viable (economic cluster). Likewise, improving self-consumption (technical cluster) can directly lower primary energy use (environmental cluster) and, consequently, yield economic benefits (economic cluster) if robust user engagement and effective automation and control systems (social cluster) are established. Structuring the KPIs into an interactive taxonomy like the present classification can provide a decision-making framework capable of guiding multi-objective optimization, ensuring technological intervention leads to improvement in all three dimensions of sustainability, including social, economic, and environmental aspects.

In summary, the proposed taxonomy offers an organized and extensible framework for evaluating smart building–grid interactive systems. The hierarchical structure of this taxonomy clarifies the role of each KPI, while the cross-cluster analysis underlines the holistic integration necessary to realize and create sustainable, resilient, and economically viable energy environments. Future research work should focus on standardizing the calculation methods, validating metrics through various scale field implementations, and preparing analysis dashboards, putting such a taxonomy into operational use in both research and practical applications.

As for the technologies’ taxonomy, the presented integrated view of the taxonomy reveals significant synergies across clusters. For instance, sensing and measurement technologies supply the data stream required for the control and automation systems for informed decision-making. Subsequently, these control systems manage and align the energy systems’ operation, including energy storage technologies, concerning real-time data, grid conditions, and forecasts derived from data and analysis tools.

The interaction between energy infrastructures (cluster 1) and other clusters is also critical. While infrastructure provides the backbone for connectivity, its real value depends on its integration with intelligent control systems (cluster 3) and the flexibility provided by storage systems (cluster 4). Likewise, simulation and machine learning tools and models (cluster 5) are effective only when paired with and trained on high-quality data by sensors (cluster 2) and implemented through building automation systems (cluster 3). The interdependence underlines the necessity for coordinated design and interoperable solutions covering physical, informational, decision-making, and actuation layers of building energy systems. The proposed structure of taxonomy for enabling technologies offers several advantages, including the establishment of a functional mapping of technologies clarifying their distinctive as well as their interconnected roles. Furthermore, it provides the possibility of promoting scalability and modularity, as each cluster can evolve independently while maintaining system interoperability and integration. It also supports benchmarking and performance comparison for state-of-the-art technologies and alternative design solutions necessary for the design of integrated solutions for maximizing the techno-economic and environmental values of smart building–grid interactions.

It should be highlighted that despite the technological readiness and elaboration of the interconnectedness of enabling technologies, several non-technical barriers, such as data ownership, interoperability across platforms, cybersecurity risk, and upfront costs, might still hinder the full implementation of smart technologies at the building and urban scale. These barriers often delay the deployment and implementation of smart technologies and underscore the need for and importance of governance and standardization in future research and policy programming.

Finally, Table 3 provides a summary and, more importantly, highlights the interconnectivity between the KPI taxonomy in Table 1 and the taxonomy for smart enabling technologies in Table 2. The link between the two taxonomies is not a simple one-to-one correspondence, but it should be comprehended as a complex and synergistic network of interconnectivity.

Table 3. The summary of interconnectivity in the proposed dual taxonomy between KPIs and smart enabling technologies.

KPI Clusters	KPIs (from Table 1)	Technology Clusters (from Table 2)	Notes on Interconnectivity
Technical Energy Efficiency	Performance Ratio; Net Direct Energy Consumption; Energy Demand Reduction	Sensing and Measurement (Energy Meters, Occupancy/IAQ Sensors); Generation, Control and Automation (BEMS/HEMS, Optimization Algorithms, Smart HVAC Controls); Data Analytics (Simulation Tools, ML Algorithms)	Sensors provide real-time data, control systems optimize operation, and smart actuation implements adjustments. Simulation and ML tools enable predictive optimization, improving energy efficiency performance indicators.
Technical Energy Flexibility	Flexibility Capacity Index; Load Shifting Factor; Peak Shaving Indicator	Energy Storage (BESS, Thermal Storage); Generation, Control and Automation (BEMS/HEMS, Control Strategies, Optimization Algorithms); Energy Infrastructures (Grid Interaction Facilities)	Storage provides physical flexibility; control algorithms decide when to shift loads; grid interaction interfaces enable flexibility participation in demand response or auxiliary services.
Technical Renewables Integration	Self-Consumption Index; Self-Sufficiency Index; Renewable Penetration Ratio	Generation, Control and Automation (PV Systems, EMS, Optimization Algorithms); Energy Storage (BESS); Sensing and Measurement (Environmental Sensors)	PV generates renewable electricity; EMS coordinates loads to maximize consumption of onsite PV; BESS stores surplus; environmental sensors supply forecasting data that enhances scheduling decisions.
Technical Grid Interaction	Grid Interaction Index; Peak-to-Average Ratio; Grid Fluctuation Reduction	Energy Infrastructures (Grid Interaction Facilities, Microgrids); Generation, Control and Automation (BEMS/HEMS, Control Strategies); Data Analytics (ML Algorithms)	Grid interfaces enable electrical and data linkage with the grid; control strategies manage scheduling to support grid stability; ML predicts grid conditions and optimizes responses.
Environmental	CO2 Emissions Reduction; Non-Renewable Primary Energy	Generation, Control and Automation (PV, Heat Pumps, EMS); Energy Storage (BESS, Thermal Storage); Data Analytics (Multi-objective Evaluation Frameworks)	Low-carbon generation reduces emissions; storage and EMS optimize renewable utilization; evaluation frameworks analyze trade-offs with cost and performance.
Economic	Life Cycle Cost; Levelized Cost of Energy; Payback Period	All Technology Clusters	Economic KPIs reflect the combined cost, performance, and operational outcomes of all deployed technologies, including investment costs, operational savings, and revenue from grid services.
Social	Smart Readiness Indicator (SRI) Score; Forced Flexibility Factor	Generation, Control and Automation (BACS, HEMS); Sensing and Measurement (Occupancy Sensors); Data Analytics (ML Algorithms)	BACS/HEMS shape overall smart capabilities; occupancy sensing and ML enable user-centric adaptive controls, minimizing discomfort during flexible operation and improving acceptance.

Table 3 summarizes several interconnectivity patterns between the dual taxonomy proposed in this research and helps better understand the application of each technology and KPI in empirical studies. The interconnectivity framework shown in Table 3 indicates that achieving the goals of smart building–grid interaction for a sustainable urban energy transition requires a systematic approach by which a comprehensive set of performance criteria (i.e., KPIs) linked to the enabling technologies can effectively measure the effectiveness of energy transition scenarios and help to design the appropriate technological roadmaps for possible enhancement of each performance criteria.

5. Conclusions

The integration of smart buildings into modern energy grids requires a transformative approach to achieve sustainable, resilient, and efficient urban energy systems. This research analyzed the recently published literature in this field to provide a structured taxonomy for key performance indicators (KPIs) and enabling technologies for smart building–grid interaction. The bibliometric insights alongside the structured taxonomy provide a comprehensive map of existing and evolving priorities, as well as establish a framework to guide future research works. This research reaffirmed a paradigm shift in research works studying the building and energy sector as dynamic grid-responsive agents rather than static entities, meaning that smart buildings are evolving as active participants in decentralized energy networks supporting energy flexibility through several strategies, including but not limited to energy management systems, control strategies, and building automation systems.

This transformation demands providing performance metrics and technologies to enable the required functions and the measurability of these strategies’ success. Thus, this research proposed a new taxonomy for KPIs across four major dimensions, including technical, environmental, economic, and social, by which each multifaceted impact of smart building–grid interaction can be evaluated. Technical KPIs form the core of operational evaluation and emphasize energy efficiency, flexibility, renewables integration, and grid interactivity, measuring several qualities of smart buildings, including but not limited to the energy efficiency and flexibility of integrated smart building–grid systems, the capacity of the building-energy systems to balance on-site energy generation with grid conditions, responsiveness to dynamic grid signals, etc.

The findings in this research also emphasize the interdependencies of collected KPIs, underlining the necessity of creating a holistic and comprehensive framework for action plans. For instance, enhancing energy flexibility as a technical goal leads to better energy performance and lower grid dependency and subsequently reduced emissions (environmental factors) and lower operational cost (economic factor), provided occupants adhere to demand response protocols (social factor). This synergy among different dimensions and KPI clusters highlights the value of the taxonomy as a decision support tool for multi-objective optimization that helps and guides stockholders to balance competing and conflicting priorities across sustainability dimensions.

Furthermore, this research proposed a taxonomy for enabling technologies in smart building–grid interaction, categorizing them into five functional clusters, including energy infrastructures, sensing and measurement, generation, control and automation, energy storage, and data analytics. The energy infrastructures, such as smart grid interfaces and microgrids, provide the physical backbone of bidirectional energy exchange, while sensing and measurement technologies, such as various types of smart meters and sensors, gather the necessary real-time data for adaptive and predictive control systems. Control and automation technological systems, coupled with data analytic tools and energy technologies together, play a pivotal role in translating the conceptual strategies and collected data from building–grid systems into actionable strategies. Like the KPI taxonomy, the interplay among clusters for enabling technologies is also critical. For instance, the sensor-derived data informs analytical tools and learning models to optimize the energy management systems and energy, HVAC operation, and storage schemes, while grid interfaces help to exchange energy and data communication within integrated building–grid systems efficiently and effectively. Such interdependencies highlight the need for interoperable and modular solutions harmonizing hardware, software, and human factors as a technical challenge that should be addressed in future research and development works to ensure the whole system’s adaptability.

The insights and practical implications of the present research extend across academia, industry, and policy development. In particular, the taxonomies presented in this article offer researchers a structured roadmap for identifying and prioritizing research gaps, such as standardizing KPIs and calculation methods, along with the validation metrics across diverse buildings and technology typologies. Industrial practitioners also benefit from the presented framework, using the presented KPIs for each performance dimension to design integrated systems that maximize techno-economic, environmental, and social values. Likewise, policymakers benefit from the presented classification of both KPIs and enabling technologies to draft policy programs and incentivizing schemes to promote sustainable solutions for smart building–grid systems.

It should be noted that the proposed taxonomy in this study was developed based on a systematic review of recent empirical studies in which the validity of the proposed KPIs and effectiveness of the reviewed enabling technologies have already been verified and validated. Therefore, given that the scope of the present study focused on reviewing and providing a dual taxonomy of KPIs and smart technologies, future research works are suggested to implement the proposed taxonomy in real case studies. While the taxonomy provides a structured foundation, it remains limited by the lack of a standardized KPI definition. Therefore, providing standard definitions and calculation methods for these KPIs highlights an important focus for future research. Future research should also focus on field validation, cross-comparisons within existing evaluation frameworks, and, in particular, the inclusion of social and behavioral dimensions. Moreover, future research and development works are suggested to focus on translating this theoretical framework into real-world applications to ensure the effective shift towards climate action and sustainable energy transition in the built environment.