Search Results (740)

Air conditioners are a critical adaptation measure against heat- and cold-related risks under climate change. However, their electricity use and refrigerant leakage increase greenhouse gas (GHG) emissions. This study developed a data-driven life-cycle assessment (LCA) framework for residential room air conditioners in Japan by integrating large-scale field operation data with life-cycle climate performance (LCCP) modeling. We aggregated 1 min records for approximately 4100 wall-mounted split units and evaluated the 10-year LCCP across nine climate regions. Using the annual operating hours and electricity consumption, we classified the units into four behavioral quadrants and quantified the life-cycle GHG emissions and parameter sensitivities for each. The results show that the use-phase electricity dominated the total emissions, and that even under the same climate and capacity class, the 10-year per-unit emissions differed by roughly a factor of two between the high- and low-load quadrants. The sensitivity analysis identified the heating hours and the setpoint–indoor temperature difference as the most influential drivers, whereas the grid CO₂ intensity, equipment lifetime, and refrigerant assumptions were of secondary importance. By replacing a single assumed use scenario with empirical profiles and behavior-based clusters, the proposed framework improves the representativeness of the LCA for air conditioners. This enabled the design of cluster-specific mitigation strategies. Full article

This study presents a comparative analysis of the potential for combined use of wind and solar energy in nine key coastal settlements on the Bulgarian Black Sea coast—Shabla, Balchik, Varna, Byala, Obzor, Nesebar, Burgas, Primorsko, and Tsarevo—selected for their diverse geographical and meteorological characteristics. The study evaluates the feasibility of implementing hybrid renewable energy systems by analyzing the average annual solar radiation and wind velocity for each location. A methodology based on physical and technical parameters is applied to determine the required installed capacity of photovoltaic systems to meet the average annual household electricity consumption of 6000 kWh. Concurrently, wind energy potential is assessed through theoretical and practical models using two turbine sizes (3 m and 6 m in diameter), which represent small-scale residential wind applications. Full article

Urban energy systems in Andean cities face growing pressure to accommodate rising electricity demand while progressing toward decarbonization and grid modernization. Residential rooftop photovoltaic (PV) generation offers a promising pathway to enhance transformer utilization, reduce emissions, and improve distribution network performance. However, most GIS-based rooftop solar assessments remain disconnected from operational constraints of urban electrical networks, limiting their applicability for distribution planning. This study examines the technical and environmental feasibility of integrating residential PV distributed generation into the urban distribution network of an Andean city by coupling high-resolution geospatial solar potential analysis with monthly aggregated electricity consumption (MEC) and transformer loadability (LD) information. A GIS-driven framework identifies suitable rooftops based on solar irradiation, orientation, slope, shading, and three-dimensional urban geometry, while MEC data are used to perform energy-balance and planning-level transformer LD assessments. Results indicate that approximately 1.16 MW of rooftop PV capacity could be integrated, increasing average transformer LD from 21.5% to 45.8% and yielding an annual PV generation of about 1.9 GWh. This contribution corresponds to an estimated avoidance of 1143 metric tons of CO₂ per year. At the same time, localized reverse power flow causes some transformers to reach or exceed nominal capacity, highlighting the need to explicitly consider network constraints when translating rooftop solar potential into deployable capacity. By explicitly linking rooftop solar resource availability with aggregated electricity consumption and transformer LD, the proposed framework provides a scalable and practical planning tool for distributed PV deployment in complex mountainous urban environments. Full article

Rapid decarbonization and decentralization of power systems are driving the integration of renewable generation, energy storage and digital technologies into unified energy ecosystems. In this context, photovoltaic (PV) systems combined with battery and hydrogen storage and blockchain-based platforms represent a promising pathway toward sustainable and transparent energy management. This study evaluates the techno-economic performance and operational feasibility of integrated PV systems combining battery and hydrogen storage with a blockchain-based peer-to-peer (P2P) energy trading platform. A simulation framework was developed for two representative consumer profiles: a scientific–educational institution and a residential household. Technical, economic and environmental indicators were assessed for PV systems integrated with battery and hydrogen storage. The results indicate substantial reductions in grid electricity demand and CO₂ emissions for both profiles, with hydrogen integration providing additional peak-load stabilization under current cost constraints. Blockchain functionality was validated through smart contracts and a decentralized application, confirming the feasibility of P2P energy exchange without central intermediaries. Grid electricity consumption is reduced by up to approximately 45–50% for residential users and 35–40% for institutional buildings, accompanied by CO₂ emission reductions of up to 70% and 38%, respectively, while hydrogen integration enables significant peak-load reduction. Overall, the results demonstrate the synergistic potential of integrating PV generation, battery and hydrogen storage and blockchain-based trading to enhance energy independence, reduce emissions and improve system resilience, providing a comprehensive basis for future pilot implementations and market optimization strategies. Full article

The increasing penetration of electric vehicles (EVs) and rooftop photovoltaics (PV) is intensifying the variability and uncertainty of residential net demand, thereby challenging real-time operation in smart grids and microgrids. The purpose of this study is to develop and evaluate an accurate and operationally relevant short-term forecasting framework that jointly models household net demand and EV charging behaviour. To this end, a Residual-Normalised Multi-Task GRU (RN-MTGRU) architecture is proposed, enabling the simultaneous learning of shared temporal patterns across interdependent energy streams while maintaining robustness under highly non-stationary conditions. Using one-minute resolution measurements of household demand, PV generation, EV charging activity, and weather variables, the proposed model consistently outperforms benchmark forecasting approaches across 1–30 min horizons, with the largest performance gains observed during periods of rapid load variation. Beyond predictive accuracy, the relevance of the proposed approach is demonstrated through a demand response case study, where forecast-informed control leads to substantial reductions in daily peak demand on critical days and a measurable annual increase in PV self-consumption. These results highlight the practical significance of the RN-MTGRU as a scalable forecasting solution that enhances local flexibility, supports renewable integration, and strengthens real-time decision-making in residential smart grid environments. Full article

Accurate electrical load forecasting is fundamental to the efficient operation of energy systems and plays a decisive role in both generation planning and the prevention of supply interruptions. Anticipating demand with precision enables energy generation and distribution to be adjusted effectively, reducing risks for both industrial and residential consumers. However, forecasting is challenged by climatic variations, demographic changes, and evolving consumption patterns, which limit the effectiveness of traditional approaches. Advanced machine learning techniques such as artificial neural networks have demonstrated potential to address these challenges, although their performance depends strongly on hyperparameter optimization. This study applies a multinodal forecasting methodology based on the Fuzzy ARTMAP network to predict short-term electricity demand at nine substations in New Zealand. The method involves an exhaustive search for network parameters, particularly the vigilance parameters ${\rho }_a$ and ${\rho }_b$ and the learning rate $\beta$, which are critical to model performance. The input data were extended with statistical measures—maximum, minimum, mean, and standard deviation—to evaluate their contribution to forecast accuracy. The results showed that the standard deviation provided the most consistent improvements among the windowing techniques, reducing the Mean Absolute Percentage Error (MAPE) in most substations. Parameter analysis further indicated that specific combinations such as ${\rho }_a$ and $\beta$ strongly influence category formation within the network, and consequently the precision of the forecasts. Full article

The rapid growth of electric vehicles (EVs) is expected to create a substantial stream of retired automotive batteries over the coming decades, offering an opportunity for low-cost stationary storage deployment. This paper quantifies how much additional photovoltaic (PV) capacity can be unlocked in Greece through the systematic use of second-life EV batteries under the new self-consumption and zero feed-in regulatory framework. First, a deterministic cohort model is developed to estimate the annual potential of second-life batteries, considering parameters like EV sales, first-life duration, repurposing eligibility, and second-life operational lifetime. The results indicate that Greece could accumulate from 3.5 GWh to 12.1 GWh of second-life batteries until 2050, depending on future EV growth rates. Next, to link battery capacity with PV unlocked potential, an hourly time-series simulation is implemented under a zero feed-in scheme, i.e., without exporting energy to the grid, indicating that each kilowatt-hour of second-life battery can unlock 0.33 kW of PVs in residential zero feed-in systems. On this basis, second-life batteries could unlock from 1.1 GW to 3.9 GW of additional PV capacity that would otherwise be infeasible. For comparison, the peak load of Greece is about 10 GW. Importantly, unlike large-scale grid-connected PV plants—where transmission system operators increasingly impose curtailments—zero feed-in installations can operate seamlessly without creating additional operational stress for the grid. Full article

This paper presents a Genetic Algorithm-based Home Energy Management System designed to exploit the energy flexibility of user-dependent loads by identifying and recommending optimal operating schedules that minimize electricity costs. To determine the most advantageous 15 min activation slot for the following day for each load, the algorithm uses as input the forecasted consumption profile of non-optimizable loads and photovoltaic generation, both obtained through an LSTM-based model, along with the contracted power, applicable tariffs, and the load profiles of the selected appliances. Unlike previous approaches, the proposed framework allows users to select which loads to optimize and define specific operational constraints. Additionally, a user-friendly interface was developed to facilitate seamless interaction between the user and the system. To validate the proposed framework, a case study was conducted on a residential household with four occupants located in Portugal, considering user-dependent flexible loads such as a washing machine, tumble dryer, and dishwasher. The results demonstrated that the developed system operated effectively, reducing electricity costs by approximately 9% compared to a scenario without the proposed solution. Full article

Due to the increasingly severe energy crisis and extreme climate conditions in recent years, the development and use of alternative clean energy sources have become increasingly important. This study evaluates the energy performance of applying residential solid oxide fuel cells (SOFCs) in a typical passive-designed residential village house in Xi’an. Furthermore, the study integrates photovoltaic (PV) systems and storage batteries with a solid oxide fuel cell co-generation system (SOFC-CGS) to enhance its overall energy performance. The results show that when the SOFC-CGS operates independently, it can provide stable electricity. However, due to its limited capacity, it only meets 43% of the total energy demand and cannot fully satisfy the heating requirements. In this energy supply scenario, the SOFC-CGS heating efficiency reaches 25%, the power generation efficiency reaches 42%, and the overall efficiency reaches 67%. After integrating the PV battery system with the SOFC-CGS, the addition of photovoltaic and battery systems boosts the energy self-sufficiency rate by 32 percent, reaching 75%. In other words, this clean energy combination can cover 75% of the household’s traditional energy consumption. In addition, the heating efficiency increases by 2 percentage points to 27%, the power generation efficiency rises by 4 percent to 46%, and the overall system efficiency improves by 6 percent to reach 73%. Furthermore, the utilization rate of the photovoltaic battery system also rises from 25% to 73%: an increase of 48 percent. Therefore, according to the analysis results, integrating PV and storage batteries with the SOFC-CGS proves to be a profitable and efficient solution for application in passive-designed village houses in Xi’an. Full article

This study examines the economic performance of residential photovoltaic systems combined with battery storage (PV-BESS) under Poland’s net-billing regime for a single-family household without subsidy support in 10-year operational horizon. These insights extend existing European evidence by demonstrating how net-billing fundamentally alters investment incentives. The analysis incorporates real production data from selected locations and realistic household consumption profiles. Results demonstrate that optimal system configuration (6 kWp PV with 15 kWh storage) achieves 64.3% reduction in grid electricity consumption and positive economic performance with NPV of EUR 599, IRR of 5.32%, B/C ratio of 1.124 and discounted payback period of 9.0 years. The optimized system can cover electricity demand in the summer half-year by over 90% and reduce local network stress by shifting surplus solar generation away from midday peaks. Residential PV-BESS systems can achieve economic efficiency in Polish conditions when properly optimized, though marginal profitability requires careful risk assessment regarding component costs, durability and electricity market conditions. For Polish energy policy, the findings indicate that net-billing creates strong incentives for regulatory instruments that promote higher self-consumption, which would enhance the economic role of residential storage. Full article

Despite growing interest in positive-energy and net-zero-energy buildings (NZEBs), few studies have addressed the integration of biobased construction with building-integrated photovoltaics (BIPV) under hot–dry climate conditions, particularly in Morocco and North Africa. This study fills this gap by presenting a simulation-based evaluation of energy performance and renewable energy integration strategies for a residential building in the Fes-Meknes region. Two structural configurations were compared using dynamic energy simulations in DesignBuilder/EnergyPlus, that is, a conventional concrete brick model and an eco-constructed alternative based on biobased wooden materials. Thus, the wooden construction reduced annual energy consumption by 33.3% and operational CO₂ emissions by 50% due to enhanced thermal insulation and moisture-regulating properties. Then multiple configurations of the solar energy systems were analysed, and an optimal hybrid off-grid hybrid system combining rooftop photovoltaic, BIPV, and lithium-ion battery storage achieved a 100% renewable energy fraction with an annual output of 12,390 kWh. While the system incurs a higher net present cost of $45,708 USD, it ensures full grid independence, lowers the electricity cost to $0.70/kWh, and improves occupant comfort. The novelty of this work lies in its integrated approach, which combines biobased construction, lifecycle-informed energy modelling, and HOMER-optimised PV/BIPV systems tailored to a hot, dry climate. The study provides a replicable framework for designing NZEBs in Morocco and similar arid regions, supporting the low-carbon transition and informing policy, planning, and sustainable construction strategies. Full article

This paper presents an energy-aware Artificial Intelligence of Things framework designed for intelligent remote device control in residential settings. The system architecture is grounded in the Power Administration Device (PAD), a cost-effective and non-intrusive smart plug prototype that measures real-time electricity consumption and actuates appliance power states. The PAD transmits data to a scalable, cross-platform cloud infrastructure, which powers a web-based interface for monitoring, configuration, and multi-device control. Central to this framework is Cross-Feature Time-MoE, a novel neural forecasting model that processes the ingested data to predict consumption patterns. Integrating a Transformer Decoder with a Top-K Mixture-of-Experts (MoE) layer for temporal reasoning and a Bilinear Interaction Layer for capturing complex cross-time and cross-feature dependencies, the model generates accurate multi-horizon energy forecasts. These predictions drive actionable recommendations for device shut-off times, facilitating automated energy efficiency. Simulation results indicate that this system yields substantial reductions in energy consumption, particularly for high-wattage appliances, providing a user-friendly, scalable solution for household cost savings and environmental sustainability. Full article

To ensure reliable, efficient and sustainable operation of modern power networks, accurate load forecasting is an important task in system planning and control. It is also a crucial task for the efficient operation of smart grids to maintain a balance between load shifting, load management and power dispatch. In this regard, this research study aims to investigate the efficiency of various machine learning models for whole-house energy consumption prediction and appliance-level load disaggregation using Non-Intrusive Load Monitoring (NILM). The primary objective is to determine which model offers the most accurate forecasts for both individual appliance consumption patterns and the total amount of energy used by the household. The empirical study presents comparative performance analysis of machine learning models, i.e., Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Gradient Boosting and Support Vector Regressor (SVR) for load forecasting and load disaggregation. This research is conducted on PRECON: Pakistan Residential Electricity Dataset consisting of 42 Pakistani households. The dataset was recorded originally as one minute per sample, but the proposed study aggregated it to hourly samples to evaluate models’ alignment with the typical sampling rate of smart meters in Pakistan. It enables the models to more accurately depict implementation scenarios in real-world settings. The statistical measures MAE, MSE, RMSE and R² have been employed for performance evaluation. The proposed Random Forest algorithm out-performs all other employed models, with the lowest error values (MAE: 0.1316, MSE: 0.0367, RMSE: 0.1916) and the highest R² score of 0.9865. Furthermore, for detecting appliance events from aggregate power data, ensemble models such as Random Forest performed better than other models for ON/OFF prediction. To evaluate the suitability of machine learning models for real-time, appliance-level energy forecasting using Non-Intrusive Load Monitoring (NILM), this study presents a novel evaluation framework that combines learning speed and edge adaptability with conventional performance metrics (e.g., R², MAE). This paper introduces a NILM-based approach for load forecasting and appliance-level ON/OFF prediction, representing its capacity to improve residential energy efficiency and encourage sustainable energy consumption, while emphasizing operational metrics for implementation in embedded smart grid systems—an area mainly neglected in prior NILM-based research articles. The results provide useful information for improving demand-side energy management, facilitating more effective load disaggregation, and maximizing the energy efficiency and responsiveness of smart grids. Full article

Many electricity providers are offering their customers an array of tariff options intended to discourage electricity consumption at specific times of the day. The problem facing a customer is whether to switch from their existing tariff to a new tariff. The aim of this paper is twofold: first, to develop two analytical methods that help residential customers evaluate when switching from a flat-rate tariff to time-varying pricing options, specifically the Time-of-Use (TOU) tariff and an event-based tariff, becomes economically beneficial, and second, to review customers’ experiences with the tariffs. The methods identify the specific consumption distributions at which the TOU or event-based tariffs are in energy- and cost-equilibrium with the domestic service tariff for residential customers. For the TOU structure, the analysis shows that customers must maintain a non-winter-to-winter-peak consumption ratio exceeding 3.0756 for cost neutrality, a condition rarely met by households with winter-dominant loads. In contrast, event-based structures require only minimal behavioral adjustments to achieve savings, with as little as 1.75% of annual consumption needing to be avoided during event periods to match domestic-service costs. Additional savings are observed with partial or full load shifting away from peak events. The findings highlight that while TOU may benefit households with high summer usage, event-based tariffs present a more practical and economically favorable option for residential customers living in the Canadian province of Nova Scotia. The paper concludes with implications for tariff selection and consumer behavior. This research will be of value to anyone considering designing a time-varying rate or having to choose between an existing flat-rate tariff and a time-varying tariff. Full article

The energy sector is currently under enormous transition, moving from fossil fuels to renewable energies and integrating energy efficiency measures. This transition can hold opportunities for new and innovative energy systems. This study presents an energetic and economic assessment of an innovative tri-generation unit working with a two-phase thermodynamic cycle. The tri-generation unit is driven by heat and is capable of providing heat at lower level, cold, and electricity to end users. The use cases—residential, day-use offices, commercial retail, and manufacturing industry—are integrated in a dynamic simulation model, indicating the operation mode of the unit. The results show that the tri-generation unit is able to provide heat and cold with an Energy Utilization Factor of 35% to 68%, depending on the use case. Solar thermal has a limited to potential to supply the unit with heat, due to the high temperature of 180 °C and the required unit operation at nighttime. The economic comparison indicates that the driving heat must be as low as possible and that savings through self-consumption is most relevant. Full article