Search Results (634)

Due to increasing demand and consumption of electricity, as well as the need to decarbonize and mitigate climate change, solar energy is an important factor in the transition to emission-free energy sources. This study focuses on identifying the most suitable locations for the construction of large solar photovoltaic (PV) power plants while respecting environmental, economic, and technical standards. The study area covers the mainland part of Southeastern Europe (796,039 km²), including the following countries: Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, North Macedonia, Albania, Greece, Bulgaria, Romania, Moldova, and Türkiye. Using geographic information systems (GIS) and remote sensing methods, nine factors (topographic, climatic, hydrological, ecological, vegetation, and anthropogenic) were analyzed with a spatial resolution of 100 m. A fuzzy analytic hierarchy process (F-AHP) pairwise comparison matrix was constructed to quantify the relative importance of the selected criteria. The F-AHP weighting results indicate that photovoltaic output (17.9%) and land use (15.7%) are the most important among the evaluated criteria. The results show that 6.7% of Southeastern Europe is very highly suitable for installing solar PV plants, with the most suitable areas located in Moldova (14.5%) and Greece (10.5%). Through spatial analysis of the final results, 24 of the most suitable locations for large-scale solar PV power plant development were identified, with a potential to generate approximately 30.2 TWh of electricity annually. In such a scenario, the forecast indicates that 24 large-scale solar power plants would supply electricity to more than 6.7 million households, corresponding to over 17 million inhabitants. The final spatial patterns provide decision-makers at the international level with a significantly more effective basis for planning solar energy development in order to increase the share of green energy and clean technologies in this part of Europe. Full article

This study develops a Service–Capacity Gap (SCG) approach to examine household electricity affordability under rising costs in Vietnam. SCG compares each household’s electricity service-cost position with its core consumption-capacity position. Using 46,375 household-year observations from VHLSS 2012–2020, the study identifies four affordability positions: service-cost pressure (SCP: 36.60%), low service-cost non-pressure (LNP: 24.46%), capacity-supported service-cost (CSS: 20.73%), and low service-cost but capacity-constrained households (LCC: 18.21%). Benchmark comparisons show that high-burden and LIHC-type diagnostics align mainly with SCP, while low-use indicators split between LNP and LCC. LNP records only 2.22% LIHC-type risk despite 49.15% falling below the low-use threshold, whereas CSS exhibits no LIHC-type risk despite relatively high electricity costs. Socioeconomic profiles further show that LCC is more capacity-constrained than LNP, and CSS has stronger capacity than SCP. Under a 30% electricity-cost shock, new exposure reaches 28.99% for CSS and 19.03% for LNP. The main diagnostic value of SCG is that it resolves two ambiguities that conventional indicators tend to conflate: low service-cost can mean either non-pressure or hidden capacity constraint, while high service-cost can mean either affordability pressure or capacity-supported service use. Policy should use SCG to target low-use households with weak capacity (LCC), avoid automatic subsidies for low-use non-pressure households (LNP), and monitor near-boundary households exposed to rising costs. Full article

This study presents a technical assessment of solar energy systems for integrated agricultural use and rural electrification. A model village comprising 30 households was considered, and high-resolution hourly load profiles were developed to characterize consumption dynamics, including peak demand and sectoral distribution across residential, agricultural, public, healthcare, and commercial users. A 60 kW photovoltaic (PV) system was designed in conjunction with an independent solar thermal installation for hot water supply. The system configuration was established through component sizing and numerical modeling, incorporating heat transfer mechanisms and operational constraints. Time-dependent simulations performed in MATLAB (R2022b) evaluated PV power output, battery storage cycling, and thermal system performance over a 24-h horizon. A comparative analysis of standalone PV, hybrid PV/T, and decoupled PV–thermal configurations was conducted based on performance and operational criteria. The results indicate that separated electrical and thermal subsystems achieve improved cost-effectiveness, enhanced reliability, and reduced maintenance requirements. The proposed approach demonstrates the technical viability of solar-based energy systems for rural applications, supporting energy autonomy, reduced fossil fuel dependence, and sustainable agricultural development. Full article

Energy-efficiency retrofit assessment for old residential buildings commonly relies on energy benchmarks, but such benchmarks cannot reveal household-level disparities in energy use. This study integrates energy-consumption benchmarks with distributional-fairness indicators to support retrofit prioritization. Monitored electricity data from 1024 households in four representative old residential building types in Chongqing were analyzed using the Dagum Gini coefficient decomposition method. The results show clear seasonal and typological differences in energy-use imbalance. The annual Gini coefficients for Types A–D were 0.34, 0.42, 0.45, and 0.40, respectively, while the overall level of imbalance generally followed the order winter > summer > transition seasons > annual average. Median energy use intensity (EUI) did not correspond directly to distributional fairness. Type B had the highest annual median EUI (3.89 kWh/m²) but not the highest Gini coefficient, whereas Type C had the lowest median EUI (3.28 kWh/m²) and the highest Gini coefficient (0.45). These findings show that benchmark-based assessment alone may misidentify retrofit priorities. A dual-benchmark diagnostic framework is therefore proposed to integrate energy-use level and distributional fairness, supporting more precise retrofit prioritization, fairer resource allocation, and sustainable renewal of old residential communities. Full article

Residential energy consumption exhibits substantial behavioral uncertainty and temporal heterogeneity, which pose challenges for demand-side management and residential load profiling. However, existing studies often focus on isolated temporal or spatial scales and predominantly employ hard clustering methods based on geometric distance metrics. To address these limitations, this study proposes a multi-scale residential load profiling framework utilizing the Gaussian Mixture Model (GMM) and nearly three years of hourly electricity consumption data from 13 residential buildings in Vancouver. First, schedule-driven and seasonal variations in residential energy consumption were examined through multi-temporal comparative analyses and paired-sample t-tests. The results indicate statistically significant differences between working-time and non-working-time energy consumption patterns in most buildings (p < 0.001). Second, individual-building clustering was performed to identify long-term intra-building daily load evolution characteristics, revealing 2–5 typical daily profiles across different households. Finally, inter-building clustering identified three representative residential groups characterized by low-energy stable patterns, high-energy intensive patterns, and intermediate commuting-oriented patterns. The average daily energy consumption levels of the three clusters were 13.11 kWh, 36.74 kWh, and 21.61 kWh, respectively. The proposed framework provides a data-driven approach for understanding residential energy-use heterogeneity across multiple scales and offers potential guidance for residential demand-side management and urban low-carbon energy planning. Full article

The building sector accounts for nearly 30% of global energy use and 28% of CO₂ emissions, with residential buildings in Canada contributing about 17% of national energy demand. In cold regions such as Labrador, approximately 82% of this consumption is associated with space heating and domestic hot water, making heating the dominant residential load, while fossil-fuel furnaces and electric baseboard heaters remain common. These conditions highlight the need for efficient and sustainable heating alternatives for cold-climate residential buildings. This study examines the design and performance of a hybrid solar photovoltaic (PV) and geothermal heat pump (GTHP) system for a typical detached home in L’Anse-au-Loup, Labrador, Newfoundland and Labrador, Canada (51.52° N, 56.84° W), with the goal of improving energy efficiency and reducing dependence on the electrical grid. Heating and cooling loads were developed using the Hourly Analysis Program (HAP 6.1), while system operation and economic performance were assessed through the Hybrid Optimization Model for Electric Renewables (HOMER Pro 3.18.3). The proposed design combines a rooftop PV array, a ground-source heat pump, and second-life lithium-ion batteries repurposed from retired electric vehicles to lower costs and support short-term energy storage. The system is modelled under grid-connected conditions to reflect realistic operation for northern households. Results show that the hybrid system can meet annual electrical and thermal needs while reducing grid consumption by more than half. Annual carbon emissions decrease by roughly 4–5 tonnes, and repurposed batteries offer a cost-effective alternative to new storage. Overall, the study demonstrates that PV–GTHP systems can provide reliable, efficient, and practical energy solutions for cold-climate homes. Full article

The growing penetration of residential photovoltaic systems increases the need for effective demand-side management strategies that improve on-site electricity utilization without battery storage. This study investigates the impact of different electric water heater control strategies on the energy and economic performance of a residential PV system. A simulation-based analysis was performed in the PV*SOL Premium environment using a 5.4 kWp household PV installation and an electric water heater as a flexible thermal load. Five operating modes with different levels of control granularity, ranging from uncontrolled operation to continuous power modulation, were evaluated under climatic conditions representative of Dunajská Streda, Slovakia. The analyzed indicators included the self-consumption ratio, self-sufficiency ratio, electricity import and export, and total variable electricity costs. Compared to the reference mode, continuous control increased the self-consumption ratio from 38.73% to 66.43% and reduced electricity export from 3340 kWh/year to 1830 kWh/year. Total variable electricity costs decreased by 31.86%, from €725.53 to €494.44 per year. The results confirm a saturation effect, where increasing control complexity provides only marginal additional benefits. Moderately complex multi-level control, therefore, represents an effective and economically attractive solution for residential PV systems without battery storage. Full article

This study investigates wavelet-based compression of smart-metering data transmitted through a software-defined radio chain implemented in LabVIEW with QPSK modulation and USRP platforms. The objective is to reduce the transmitted payload while preserving the fidelity of the reconstructed electrical load profile. The work combines a mathematical formulation of the DWT-based compression and reconstruction process, a controlled scenario evaluation, and an experimental validation on an SDR testbed. The scenario analysis shows that the compression–reconstruction trade-off is best achieved in an intermediate operating region, where excessive coefficient removal increases reconstruction error despite higher nominal reduction. In the laboratory SDR campaign, Haar wavelet order 1 at the LabVIEW coefficient-retention setting 59 was selected as the most balanced representative configuration, achieving a 60.2% unit-based compression ratio, 10.61% relative error, $\mathrm{RMSE}=31.86$ and $\mathrm{SNR}=16.98\mathrm{dB}$. This selection refers to the physical SDR implementation and should not be confused with the public-dataset validation, where bior4.4 level 8 with 40% retained coefficients provided the best offline compression–reconstruction trade-off. Under the tested USRP/LabVIEW configuration, the 5 GHz setup showed shorter channel occupation time than the 915 MHz setup, with lower measured coverage in the same laboratory campaign. The additional validation using the public UCI Individual Household Electric Power Consumption dataset confirmed that DWT compression can preserve load-profile structure under substantial coefficient reduction. Overall, the results indicate that wavelet compression is technically feasible for smart-metering transmission over SDR links when the wavelet family, order, coefficient-retention setting, and radio-link operating conditions are jointly considered. Full article

When a fuel briquette is pressed using solar electricity in summer and burned for heating in winter, the briquette functions as a seasonal energy store—without batteries, self-discharge, or capital investment in storage infrastructure. This paper quantifies such “indirect energy storage” at an operating briquette production facility in Sumy, Ukraine, using 2024 operational data from a 34 kW hybrid solar power plant integrated into the production process without battery storage under continental climate conditions (50°55′ N) and full-scale military conflict. The objective was to determine the contribution of the solar power plant (SPP) to energy supply, analyse the structure of electricity consumption, and quantify the mechanism of indirect accumulation of renewable energy through transformation into solid biofuels. The study tested two hypotheses: (H1) that integration of a solar power plant into industrial daytime operation (6:00–22:00) achieves a self-consumption rate close to 100%, displacing grid electricity without curtailment or storage losses; and (H2) that the solar fraction embedded in produced briquettes constitutes a quantifiable mechanism of indirect seasonal energy storage despite a temporal mismatch between solar peaks (summer) and product demand (winter). Methods included statistical analysis of monthly and intraday operational data; Pearson correlation analysis between solar generation and production cycles; energy audit of production processes; decomposition of specific consumption into pressing and packaging components; and a simple economic assessment (NPV, IRR, LCOE, payback) with sensitivity analysis. Annual production reached 1222.975 t of briquettes. Total specific electricity consumption (including two short packaging campaigns in June and July only) was 141.3 ± 12.6 kWh/t (CV = 8.9%). After deducting 4962 kWh of dedicated packaging electricity (2.9% of annual consumption), the specific consumption for briquette pressing alone was 136.7 ± 5.0 kWh/t (CV = 3.7%)—within the European benchmark range of 80–150 kWh/t for wood densification, with tight monthly variation indicating a stable, well-tuned pressing operation throughout the year. The SPP supplied 18.3% of total annual electricity, peaking at 33.06% in May and averaging 29.95% from March to August. Intraday analysis of 530 five-minute intervals confirmed a 100% self-consumption rate across all seasons (H1 supported). A total of 223.4 t of briquettes containing accumulated solar energy were produced during the spring–summer period. A weak negative correlation (r = −0.28) between monthly SPP generation and briquette production was observed but did not reach statistical significance (p = 0.385); this descriptive—rather than causal—relationship is consistent with the expected temporal shift between summer surpluses and winter demand, and is itself a signature of indirect rather than direct energy coupling (H2 supported in a descriptive sense). The compound efficiency along the solar-to-stored-fuel chain was estimated at approximately 68%, providing a quantitative indicator for the indirect-storage concept. Economic analysis yielded a simple payback period of about 3 years, NPV (20 yr, 12%) ≈ 1.15 million UAH, IRR ≈ 33%, and LCOE ≈ 3.28 UAH/kWh—61% below the prevailing industrial tariff of 8.45 UAH/kWh—with sensitivity analysis showing positive NPV across ±20% variation in electricity price and ±15% in CAPEX. To the best of the authors’ knowledge, this is the first empirical quantification of biomass-solar integration as a seasonal energy buffer operating without battery storage. The solar energy accumulated in briquettes is sufficient to heat 56–74 households for a full winter season. Regional scaling of the present configuration—under explicit assumptions of comparable facility sizes and operating regimes—could in principle provide fuel for 15,000–20,000 households (8–12% of regional heating needs during energy crises). These findings are directly relevant to post-conflict energy recovery and to regions where attacks on energy infrastructure have left solid biofuels as the primary available heating source. 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

In this study, we investigate the impact of electric energy storage (EES) on phase line power flow symmetry and photovoltaic (PV) energy utilization in prosumer three-phase four-wire integrated household systems. The analysis is based on high-time-resolution (1 s) experimental data collected from a real household grid and subsequent simulations of energy flows using MATLAB/Simulink software. Two converter operation strategies were evaluated: the conventional symmetric mode and the asymmetric mode developed by the authors based on an adaptive power flow management algorithm. For both strategies, the impact of EES capacity on imbalance in the distribution system operator (DSO) grid was investigated. The methodology analyzes energy flows in each phase line separately, allowing for a detailed assessment of the imbalance between phase line phenomena and their impact on local energy consumption. Key performance parameters used for the efficiency evaluation include the self-consumption and self-sufficiency rates, which quantify the share of locally generated energy consumed within the household and the degree of independence from the DSO grid. The results show that combining adaptive asymmetric inverter control with appropriately sized energy storage allows for more efficient on-site utilization of PV energy, which, at the same time, improves the load symmetry of the phase lines in the DSO grid. 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

In Brazil, more than 80% of households rely on electricity for water heating, representing approximately 13% of residential electricity consumption and significantly contributing to peak grid demand. As a prominent alternative for supplying household thermal energy and reducing grid stress, this study experimentally evaluates, under tropical climate conditions, the performance of a modular water-based photovoltaic–thermal (PVT) system and compares it with a conventional photovoltaic (PV) system operating simultaneously under identical environmental conditions. The PVT system, based on commercial PV modules coupled to roll-bond heat exchangers, a storage tank, and a shower outlet, was tested under three hydraulic regimes: natural thermosiphon, closed-loop, and Forced circulation. A dedicated ESP32-based data acquisition system, integrated with a cloud platform, continuously monitors electrical, thermal, and meteorological variables. Results show that PVT modules exhibit a small electrical efficiency reduction due to increased cell temperatures, which is largely compensated by the simultaneous thermal generation, yielding overall efficiency gains of 74.04%, 76.53%, and 7.62% over the reference PV system for Normal, Forced, and Closed circulation, respectively. The comparative analysis identifies Forced-circulation scheduling and the matching between thermal generation and consumption as key factors for performance optimization. The findings provide practical guidelines for deploying PVT systems to replace electric showers in tropical regions, reducing residential electricity consumption and mitigating peak-demand stress on the grid. Full article

This study presents a comprehensive clustering analysis of residential prosumer profiles for optimizing control and peer-to-peer (P2P) energy trading in community renewable energy systems (CRES). Using data from 25 prosumer households equipped with rooftop solar photovoltaic (PV) systems and electric vehicle (EV) charging capabilities, this study implements and compares k-means and spectral clustering algorithms to identify optimal segmentation strategies for prosumer energy management. K-means clustering identifies seven practical prosumer categories with a silhouette coefficient of 0.17, while spectral clustering achieves superior mathematical separation with a silhouette coefficient of 0.275 in ten clusters, though producing six singleton outliers. The k-means solution demonstrates three primary prosumer categories: net producers, net consumers, and balanced profiles. Cluster size variation requires adaptive optimization, while singleton outliers need custom strategies. EV ownership impact consumption, so future proliferation demands dynamic clustering, and these findings will guide metaheuristic algorithms for energy trading and pricing. Full article

Electricity inconsistencies in mini-grids, stemming from meter drift, telemetry faults, topology misconfiguration, non-technical losses, phase imbalance or data manipulation, often emerge as weak, spatially distributed deviations that are difficult to anticipate, yet timely warning is important for future monitoring frameworks in rural electrification and island mini-grids. Existing approaches either apply post hoc threshold-based alarms to individual channels or employ deep learning models that treat metering points independently, ignoring the spatial coupling imposed by the electrical topology and lacking mechanisms to enforce physical feasibility under scarce labeled data. This paper introduces PhysGTT, a Physics-Guided Self-Supervised Graph Temporal Transformer that models the mini-grid as a topology-aware graph and combines a residual Graph Convolutional Network encoder with a temporal Transformer. PhysGTT employs self-supervised pretraining via masked multi-sensor reconstruction and contrastive regime alignment to exploit unlabeled operational data and incorporates gradient-coupled physics regularization through power-balance, voltage-bound and ramp-rate penalties applied to a learned reconstruction head, while producing constraint-level attributions that identify the dominant physical violation pattern for each forecast. PhysGTT is evaluated on a proxy benchmark derived from the UCI Individual Household Electric Power Consumption dataset and on the IEEE 13-node test feeder simulated in OpenDSS and it is compared under identical experimental protocols with eight baselines spanning recurrent, graph-temporal and unsupervised architectures. On the proxy benchmark, PhysGTT achieves an AUC-ROC of 0.8959, an F1-score of 0.8307 and a False Alarm Rate of 0.41%, improving the F1-score by 2.2% relative to the strongest recurrent baseline (GRU) and by up to 15.2% relative to the LSTM baseline, while reducing the False Alarm Rate by approximately 52% relative to the LSTM baseline. On the IEEE 13-node feeder, PhysGTT attains an AUC-ROC of 0.9016 and an F1-score of 0.8361. These results indicate that integrating topology-aware encoding, self-supervised pretraining and physics-guided learning provides a promising and interpretable framework for proactive inconsistency forecasting under synthetic and feeder-simulation benchmarks, although field validation on naturally occurring faults remains necessary. Full article