Search Results (249)

Europe’s solar photovoltaic (PV) capacity is expanding rapidly, raising a key question: how much PV can each national electricity system actually absorb? Most existing assessments rely on annual or seasonal averages, which overlook the hour-by-hour match between PV generation and demand that ultimately limits feasible deployment. This study quantifies the demand-constrained PV potential of 38 European countries and how it varies across regions. Hourly PV generation is simulated in PVsyst and matched against national hourly demand from ENTSO-E. Feasible capacity is defined as the largest installation whose output never exceeds demand in any hour of the year. This system-level, time-resolved method yields operationally constrained estimates rather than purely physical potential. The 38 countries could feasibly deploy about 614 GWp of PV, generating around 678 TWh per year without exceeding hourly demand. Regional differences are pronounced: southern Europe benefits from superior solar resources, while northern and eastern regions face seasonal and infrastructural challenges. These findings underline the importance of grid modernization, energy storage, and cross-border integration. The estimates form a conservative baseline; they exclude drivers such as electric-vehicle (EV) deployment, demand-side flexibility, battery energy storage, latent demand growth, power export, and building-integrated photovoltaics (BIPV), whose inclusion would expand the feasible potential. This study offers a transparent comparative framework to guide policy, investment, and system planning for Europe’s carbon-neutral energy transition. Full article

Operational energy-forecasting pipelines require traceable execution from data ingestion to monitoring, yet few studies evaluate whether such pipelines continue to enforce quality controls when inputs or configurations are degraded. This study implements a previously proposed seven-phase forecasting lifecycle as a configuration-driven system on a self-hosted ClearML platform. The implementation is organised into five architectural domains: data and configuration, lifecycle phases and gates, orchestration, document artifact governance, and human-in-the-loop oversight. The pipeline is evaluated through six runs on four years of hourly electricity-consumption data from a Norwegian kindergarten building. Two baseline runs, in automatic and human-in-the-loop modes, demonstrate end-to-end execution and produce an XGBoost champion model with a 24-h-ahead test RMSE of 1.19 kW. Four controlled variants then test the validation-route logic by injecting missing data, shuffled consumption values, restrictive feature selection, and missing foundation-document sections. The first three variants are detected by phase-level sub-checkpoints, while the fourth is detected by Gate 0 through document-structure validation. The runs exercise revise-and-recover, override-then-terminate, and immediate-abort response pathways. The evaluation therefore demonstrates lifecycle execution, validation-route behaviour, and artifact traceability under controlled conditions; claims about live-deployment performance and multi-building generalisation are out of scope and identified as next steps. Full article

Achieving deep decarbonization of the residential building sector is essential for meeting Canada’s climate commitments and Net Zero targets. However, large-scale residential retrofit planning is often constrained by the time, cost, and expertise required for detailed building energy modelling. This study evaluates the applicability of a typology-based retrofit planning tool developed by Homes to Zero (HTZ) as a simplified alternative to conventional simulation-based analysis. Two representative Canadian residential archetypes—a detached bungalow and a two-storey semi-detached home located in Toronto—were analyzed using both the HTZ platform and detailed hourly energy simulations conducted in eQuest (DOE-2.2 engine). Baseline energy consumption and greenhouse gas (GHG) emissions were first compared across the two modelling approaches. Results show strong agreement for the bungalow case, with differences of less than 1% for electricity and natural gas consumption and approximately 4% for total emissions. For the two-storey dwelling, baseline electricity estimates were identical while natural gas consumption differed by approximately 17%, highlighting the sensitivity of physics-based simulations to envelope and operational assumptions. Retrofit scenarios were then compared using single-measure GHG reductions derived from HTZ and incremental simulation results from eQuest. While both tools identified electrification through air-source heat pumps as the dominant emission-reduction strategy, differences were observed in the magnitude of savings for envelope upgrades and secondary measures. The HTZ platform also provides approximate retrofit cost estimates, enabling order-of-magnitude budgeting, whereas eQuest requires separate costing analysis. This study is framed as a screening-level benchmark rather than a full validation exercise, highlighting the trade-off between scalability and modelling fidelity in residential retrofit planning. The results suggest that typology-based tools can provide credible screening-level guidance for residential retrofit planning and large-scale policy analysis, while detailed simulation remains valuable for evaluating integrated retrofit packages and design-level decisions. Full article

Temporary container buildings are widely used because of their rapid construction, flexible deployment, and suitability for construction-site accommodation, emergency facilities, event housing, and other short-term scenarios. However, their energy-saving design still lacks specialized standards. Key parameters such as insulation thickness, window thermal performance, airtightness, and split-air-conditioner efficiency are often selected empirically, which makes it difficult to balance initial investment and operating cost over the actual service life. To address these issues, this study proposes a service-life cost-based coordinated optimization framework. The framework couples DeST hourly load simulation, a TRNSYS-derived dynamic energy-efficiency-ratio (EER) model for split-type air conditioners, an economic model including initial investment and electricity operating cost, and an SLSQP-based optimizer. Field measurements from a three-story container dormitory in Haidian District, Beijing, collected in August and December 2023, are used to validate the HVAC electricity-consumption model through cumulative electricity-consumption errors and CV(RMSE). Using a south-facing single container building in Beijing as the base case, optimization is conducted for design service lives of 1–10 years and further compared under different electricity-pricing models and climate regions. The results show that, within the allowable parameter ranges, the proposed method can reduce service-life cost by up to approximately 32%. In the Beijing 2-year case, the optimized scheme reduces service-life cost by 39.9% compared with the permanent-building-code benchmark and by 11.4% compared with a market sample. The results demonstrate that coordinated envelope–HVAC optimization can avoid redundant initial investment and provide scenario-adaptable design support for temporary container buildings. Full article

The emergence of citizen energy communities under the European Clean Energy Package is creating new opportunities for neighboring households to collectively reduce electricity costs through local energy sharing. This paper presents a distributed multi-agent energy management system for a two-household residential energy community in which each household is equipped with photovoltaic generation and a battery energy storage system operating under realistic hourly-varying electricity prices. Each household is managed by an independent Deep Q-Learning agent that learns a cost-optimal charging and discharging policy using only local observations. In parallel, a coordination agent, implemented on the SPADE platform with XMPP-based messaging, oversees real-time peer-to-peer energy transfers between households, enabling energy exchange whenever one household has surplus generation and another faces a deficit. The two households are deliberately configured with complementary profiles: one has higher PV generation capacity while the other has higher energy consumption. This setup creates natural opportunities for local energy sharing between them. Performance is assessed through a three-level evaluation framework: (i) individual household economics (cost reduction, battery management, grid exchanges), (ii) coordination efficiency (transfer frequency, direction, and volume), and (iii) aggregate community performance, which isolates the added value of peer-to-peer sharing beyond what each household achieves through individual BESS optimization. Numerical experiments using GEFCom2014 solar generation data, synthetic residential load profiles calibrated following documented consumption patterns, and day-ahead price signals representative of the Spanish electricity market demonstrate that both Deep Q-Learning agents independently learn effective charge/discharge strategies aligned with price signals and PV availability. They also show that the coordination layer further reduces community grid dependence by routing surplus energy locally rather than exchanging it with the main grid at less favorable rates. The results confirm that a well-engineered integration of decentralized reinforcement learning with a lightweight coordination protocol can deliver measurable economic benefits in realistic residential energy communities without requiring centralized training, shared data, or complex multi-agent reinforcement learning architectures. Full article

Efficient electricity distribution in rural areas is strongly affected by seasonal agricultural energy demand, particularly in irrigation-intensive regions where electricity consumption increases substantially during summer periods. Conventional transformer operation strategies in such rural grids often fail to adapt to seasonal load variability, leading to unnecessary idle operation, increased technical losses, and reduced infrastructure efficiency. Existing approaches generally rely on static assumptions or simulated data, limiting their ability to represent real irrigation-driven seasonal load asymmetry. To address this issue, this study proposes a data-driven multi-objective seasonal transformer scheduling framework using a bio-inspired Artificial Immune System (AIS) algorithm. The model was developed using two years of empirical hourly SCADA load data and transformer operation records obtained from a real 380/154 kV TEİAŞ transmission substation in Central Anatolia, Türkiye. Hourly SCADA measurements were used for seasonal load characterization and objective-function evaluation, while transformer scheduling decisions were defined at the seasonal operational level. The proposed AIS-based scheduling strategy reduced annual technical energy losses by approximately 5.4 GWh, decreased operational costs by 10.81 million TL (≈360,000 USD), and lowered carbon emissions by about 2270 metric tons of CO₂ compared with conventional static transformer operation. The study presents a proof-of-concept framework integrating empirical SCADA measurements with AIS-assisted seasonal transformer scheduling for practical utility-scale operational planning in irrigation-dominated rural electricity networks. Full article

Despite the widespread deployment of combined PV–BESS systems in community buildings, the distinct contributions of each technology to energy consumption reduction and electricity cost savings remain poorly quantified under real operational conditions. Three years of measured hourly operational data (2023–2025) from a twelve-building mixed-use complex in Siheung-si, South Korea, were analyzed to disaggregate the contributions of a 105.84 kW PV array and a 216 kWh BESS operating under a time-of-use (TOU) electricity tariff. PV and BESS contributions were separated by computing hourly energy flows from measured generation, charging, and discharging data. PV self-consumption accounted for all energy savings, totaling 270,028 kWh over the study period, while the BESS recorded a net energy loss of −7833 kWh due to round-trip efficiency losses. In contrast, the BESS contributed to electricity cost reduction by shifting on-peak consumption to off-peak charging periods, accounting for 13–15% of total annual cost savings. Total electricity cost reduction over three years reached $31,020, with on-peak periods contributing 70.3% of savings. These results establish that PV and BESS serve fundamentally distinct functions: PV reduces both energy consumption and electricity costs through direct self-consumption, while BESS operates as a cost-shifting mechanism through TOU arbitrage without reducing net energy use. The quantitative results provide a practical basis for evaluating PV–BESS systems in community-scale buildings under real-world tariff conditions. Full article

Against the backdrop of the global advancement of carbon neutrality goals and the energy transition in the building sector, zero-carbon buildings have emerged as pivotal enablers for achieving carbon neutrality in the construction industry. The rule-based scheduling of energy storage systems (ESS) is critical to enhancing energy efficiency and economic performance of buildings. This study takes the Jinan Zero-Carbon Operation Center Project in Shandong Province as the research object, developing a comprehensive technical framework covering the entire process from design to operation, and investigates the rule-based design and ESS scheduling strategies in response to Shandong’s newly implemented seasonal time-of-use (TOU) electricity pricing policy. First, core performance indicators are defined in accordance with national evaluation standards for zero-carbon buildings. Hourly building energy loads and photovoltaic (PV) generation profiles are simulated over a full year, which serves as the basis for determining the optimal PV installed capacity and ESS sizing. Second, an ESS scheduling strategy integrating PV generation forecasting and the seasonal TOU electricity price structure is formulated, with clear charging and discharging logic defined. Finally, the operational and economic performance of different scheduling modes are evaluated and compared through case studies. The results show that the annual PV generation ratio reaches 101.38%, with a self-consumption rate of 73% and a self-sufficiency rate of 72%, all meeting the core requirements for zero-carbon buildings. Compared with the conventional real-time scheduling mode (Mode 1), the proposed optimized mode (Mode 2) that incorporates TOU pricing and PV forecasting achieves an annual operational cost saving of 367,349 CNY, corresponding to a reduction of 47.02%. Distinct seasonal variations in core indicators are also observed: the PV generation ratio is lower in summer and winter but the self-consumption rate is higher, with the opposite trend in spring and autumn. The proposed technical framework and scheduling strategy provide practical guidance for the design and operational optimization of zero-carbon buildings and offer decision-making support for ESS operation under TOU electricity pricing policies. Full article

The emergence of Artificial Neural Networks (ANNs) and their deep learning form called Artificial Intelligence (AI) opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach, in a book entitled Retrofitting, the Energy and Environment of Buildings (Gruyter Publishers), and presenting generalized AI modeling in the following paper. This concept uses a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and test) for optimalization to hourly data collected for one full year. The non-residential buildings are less affected by the space occupants. This paper examines the feasibility of a uniform, climate modified technology, as our objective is to create a universal and affordable approach to buildings assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption linked with weather conditions was born. This network is to forecast the electricity consumption for buildings linked to the local weather conditions, but different categories of buildings are put together in one set. While this will lower the large set precision, still our question is if such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use. Full article

Accurate day-ahead estimation of residential demand response (DR) potential is essential for load aggregators participating in electricity markets. It is also difficult to estimate because public residential datasets rarely contain observed DR event labels and household flexibility is shaped by heterogeneous, weather-sensitive consumption behavior. This paper proposes an appliance-agnostic two-stage framework for forecasting residential DR potential from aggregate hourly load and weather data. In the first stage, a thermal-response model estimates household heating and cooling sensitivities and converts thermostat-setback assumptions into synthetic DR-potential targets. Because these targets are model-derived proxies rather than measured DR events, the reported forecasting errors should be interpreted in terms of accuracy against a physically motivated synthetic target. In the second stage, the synthetic target sequence is forecast using a mixture of KAN experts (MoKE). The architecture combines Wavelet-KAN, Fourier-KAN, and RBF-KAN experts through sparse top-k routing with reversible instance normalization, allowing the model to represent local irregularities, recurrent daily/seasonal structure, and smooth nonlinear response regimes in the same forecasting layer and these forecasting characteristics are absent from traditional deep learning forecasting models. The framework is evaluated on the UMass residential dataset, which contains hourly electricity and meteorological measurements from 114 apartments collected during 2015 and 2016, using a 24 h day-ahead forecasting horizon. Across both winter and summer evaluation windows, the proposed model achieves the lowest error among all benchmark methods, outperforming TimesNet, Informer, N-HiTS, FEDformer, PatchTST, and TCN across MAE, MAPE, RMSE, and sMAPE. In particular, MoKE attains MAE values of 3.19 in winter and 3.18 in summer, demonstrating stable predictive accuracy under seasonally distinct operating conditions. These results show that heterogeneous KAN experts offer a feasible method for residential DR forecasting when appliance-level metering and observed event-level DR measurements are unavailable. Full article

This study investigates integrating an air-to-water heat pump (HP) into an existing office building served by a district heating (DH) system to improve energy performance and reduce environmental impact. The system was modelled using Polysun software, considering two operating scenarios: a conventional configuration based solely on DH and a hybrid configuration combining DH with a HP. The analysis was performed using hourly simulations over a typical meteorological year, allowing a detailed evaluation of system behaviour under varying climatic conditions. The results indicate that the hybrid system reduces total energy consumption by approximately 24%, while natural gas consumption decreases by about 36%. Although electricity consumption increases due to HP operation, the overall energy performance is significantly improved. The HP operates efficiently within the analysed temperature range, with COP values ranging from 1.8 to 3.0 and a seasonal performance coefficient of approximately 3.6. The system ensures full coverage of the heating demand, with a negligible deficit, confirming appropriate sizing and control strategy. From an environmental perspective, the hybrid configuration results in approximately 29 t CO₂ per year less than the conventional system. These results demonstrate that integrating HPs into existing DH systems can represent a viable solution for similar buildings under comparable operating conditions. Beyond the quantified energy and environmental benefits, the novelty of the study lies in evaluating a hybrid solution under real operating conditions affected by DH instability. The results highlight practical implications for system resilience, operational flexibility, and the applicability of this retrofit strategy to existing buildings connected to conventional DH networks. Full article

Addressing the challenges of fluctuating renewable energy integration and stable green ammonia production, this study develops and optimizes a deeply integrated system comprising Solid Oxide Electrolysis Cells (SOEC), Liquid Air Energy Storage (LAES), Air Separation Units (ASU), and Haber–Bosch (HB) synthesis. We constructed a simulation model in Aspen Plus incorporating Ru/C catalyst kinetic parameters to analyze key subsystem parameters and optimize operating conditions based on maximized economy and efficiency. At the integrated system level, a parametric analysis of ammonia condensation temperature was further conducted to investigate the coupling characteristics. Using real power output data from Inner Mongolia, we formulated a dynamic energy scheduling strategy satisfying 24-h self-balancing constraints. Results indicate that a system producing 1415 tons of ammonia per day achieves a maximum hourly integrated profit of 69,838 CNY under optimal conditions: a hydrogen-to-nitrogen ratio of 2.98:1, operating pressure of 169 bar, reactor inlet temperature of 380 °C, and ammonia condensation temperature of −9 °C. Increasing the LAES throttle valve outlet pressure from 1 bar to 9 bar improved round-trip efficiency from 52.65% to 72.18%. The integrated-level parametric analysis reveals that the specific electricity consumption per unit mass of ammonia exhibits a non-monotonic trend with a minimum of 8.67 kWh/kg at −10 °C, reflecting the trade-off between refrigeration power consumption and cold energy recovery. In dynamic scheduling scenarios, the system maintains a maximum constant load of 45.78 MW with a steady-state liquid ammonia output of 6543 kg/h. This work optimizes both economic performance and system stability, providing a significant reference for the large-scale development of green ammonia systems. Full article

This study addresses the critical issues of rigid energy use and insufficient demand-side responsiveness in office buildings’ Variable Refrigerant Flow (VRF) systems under complex summer conditions. Existing research lacks fine-grained characterisation of short-term load fluctuations and often fails to accurately couple energy efficiency with humidity-adapted thermal comfort. To fill this gap, this paper proposes an integrated Model Predictive Control (MPC) framework driven by load characteristic identification and a novel hybrid prediction model. First, based on actual hourly metered data (683,280 records), K-means clustering was employed to identify three typical load patterns, pinpointing short-term peak loads in core office zones as the primary target for flexible regulation. Second, a high-precision GS-DBO-ELM prediction model—integrating Grid Search and Dung Beetle Optimisation—was developed to capture the nonlinear dynamics of VRF energy consumption and Predicted Mean Vote (PMV). The model achieved an R² of 0.99 with relative errors constrained within ±5%. Finally, a multi-objective MPC strategy, solved via an improved Artificial Hummingbird Algorithm (HAGSAHA) and weighted by the Analytic Hierarchy Process (AHP), was implemented to dynamically adjust zone-level temperature setpoints. Results demonstrate that the proposed MPC strategy reduces daily cooling energy consumption by 7.95–10.69% and peak loads by 15.3%, while maintaining strict thermal comfort (PMV within ±0.5). Under a time-of-use pricing mechanism, the flexible scheduling strategy achieved a 12.37% total electricity reduction and a 9.54% reduction in operating costs. This work provides a highly replicable, climate-tailored solution for low-carbon, flexible energy management in public buildings. Full article

Following the massive power cut in Spain on 28 April 2025, questions have been raised about the reliability of energy generation infrastructure in the face of the variability of renewable energy sources. On the other hand, the market penetration of electric vehicles and their charging requirements implies the need for knowledge about the availability of electric generation technologies. This research work presents a macro-level analysis of the availability factor of electricity generation mix, applied to the case of Spain and based on data collected between 2019 and 2024. Using hourly generation and installed capacity data, a methodology is developed to estimate the seasonal and daily availability of the main generation technologies: photovoltaic, solar thermal, wind, hydroelectric, nuclear, combined cycle, coal and others. The analysis reveals that conventional sources, such as nuclear and combined cycles, exhibit low variability, with daily fluctuation of less than 1%. In contrast, renewable sources show significant variability. Photovoltaic availability increases from 22.6% ± 1.3% in the early morning to 57.8% ± 0.6% during summer afternoons, while solar thermal energy reaches a maximum of 78.5% ± 1.3% under the same conditions. The results highlight the uncertainty generated by the high penetration of renewable energy and the challenges posed by balancing generation with demand, particularly given new consumption patterns influenced, for example, by electric vehicles, battery storage and green hydrogen, among others. The integration of probabilistic planning frameworks into infrastructure development and the extension of this analysis to the provincial level, together with the incorporation of restriction and self-consumption scenarios involving constraints and self-consumption, will help to ensure the robust operation of the grid in the future. Full article

This article assesses the potential for intra-day redistribution of the electrical load of water intake systems under different electricity tariff models, using water supply systems in Belarus and China as case studies. It demonstrates how tariff policy influences the electrical load profile of a water intake system and quantitatively evaluates the economic effect of optimizing the operating modes of pumping equipment. The analysis is based on daily profiles of electric power and water supply. For the Belarusian water supply system, data for 2019 were considered, corresponding to the baseline operating mode without targeted load management, and data for 2023 were considered after the transition to dispatch-based control of well activation with account taken of tariff constraints (without automation tools). For the Chinese water intake system, hourly data for 2025 were used. The load redistribution potential was assessed on the basis of lagged correlation between power and water supply profiles. In addition, the F-index was applied as an aggregated diagnostic indicator intended for the comparative assessment of potential load transferability across technological stages, taking into account their share in total energy consumption. For the Chinese case, it was shown that the maximum correlation between water supply and electricity consumption across all technological stages is achieved near zero lag, which indicates a high adaptation of system operating modes to current demand; at the same time, the R values were 0.19 for reservoir intake, 0.86 for water treatment, and 0.51 for the pumping station. In the Belarusian case, for the first-lift stage, the maximum correlation is shifted by −6 h relative to zero lag, indicating a less rigid linkage of pump operation to current demand and a more inertial response of the system. A comparison of 2019 and 2023 for the Belarusian facility showed that targeted regulation of well activation and load redistribution across tariff zones reduced the total electricity cost by 1.58%, confirming the potential for further optimization of electricity consumption regimes. Full article