Search Results (167)

Accurate design and performance assessment of solar thermal domestic hot water systems coupled with a heat pump auxiliary typically requires transient simulation, as the system’s behavior depends on multiple interactions among collector characteristics, storage stratification, control logic, weather, and draw-off timing. Monthly methods such as the f-chart are useful for first-pass estimates, but they do not resolve stratification, thermostat operation, or demand timing, and they may become inaccurate for stratified thermostat-controlled systems. Direct comparisons of locally inspectable symbolic and black-box surrogate families for this system class remain limited. A 10,982-case development dataset was generated from minute-resolved annual MATLAB simulations, parameterized by collector area, optical efficiency, and first- and second-order loss coefficients. Three surrogate families were benchmarked under a unified protocol, random forest-assisted shape-constrained symbolic regression (SR), feed-forward artificial neural network (ANN) models, and Automatic Learning of Algebraic Models for Optimization (ALAMO), with the f-chart used as a monthly reference method. The targets were the 12 monthly solar fractions under the direct solar heat definition and the corresponding annual mean solar fraction, evaluated on the same independent 991-case test set. SR achieved the lowest average error (mean absolute percentage error, MAPE = 0.82%; root mean square error, RMSE = 0.006), followed by the ANN (MAPE = 2.07%, RMSE = 0.028) and ALAMO (MAPE = 3.67%, RMSE = 0.060), with Nash–Sutcliffe efficiency (NSE) values above 0.98 for all models. Evaluation times were 0.0026–0.124 s per target, compared with about 1000 s for one full-year simulation. These results define the study as a common protocol benchmark within the studied simulator-defined envelope. SR gives the strongest accuracy with local symbolic inspectability, the ANN remains the flexible retrainable option, and ALAMO provides compact algebraic evaluation with the shortest learned model runtime. 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

Internal thermal load fluctuations and variations in occupant density affect the performance of Hybrid Geothermal Heat Pump (HGHP) systems. Traditional control strategies cannot provide the rapid adjustments needed to operate efficiently in real time and can be inefficient, leading to increased energy consumption and reduced thermal comfort. A data-driven fuzzy logic control framework is developed in this paper to dynamically adjust the performance of an HGHP system in real time as a function of occupancy and environmental conditions (e.g., temperature and humidity differences). The controller analyzes input data related to real-time outdoor ambient conditions like temperature, humidity and occupied spaces; a real-time flow sensor attached to the occupants of the building (a count of the number of occupants currently in each occupied space); and the coefficient of performance (COP) of the HGHP system, and uses the analysis to generate a “smart” control decision for the following device types: variable speed drive (VSD), fan number, operating modes, system control and valve positions. The controller also controls the overall system. The model was developed and simulated in MATLAB Simulink^®, with realistic system parameters, and validated and calibrated using operational data from an HGHP system at a university, based on operating conditions. The simulation results indicate that our fuzzy controller achieves higher energy efficiency for thermal comfort than traditional thermostat-based controls, with COP improvements ranging from 7.36% to 11.76% and power consumption reductions between 4.13% and 8.55% across various occupancy scenarios. The improved COP also demonstrates the device’s responsiveness and effectiveness, even under frequent changes in occupancy patterns (dynamic occupancy), making it suitable for use in automated climate control systems in modern buildings. Full article

This study presents the metrological validation of encapsulated DS18B20 digital temperature sensors. Eight units were tested, and seven were analysed (sensor 8 was excluded owing to a systematic failure). The evaluation was performed using a standard comparison calibration, where T_ref was defined as the mean of two calibrated Pt-100 probes in a Julabo DYNEO DD 601F thermostatic bath, following the TH-001 procedure of the Spanish Centre of Metrology (CEM). Four validation tests were performed: Test 1 (E1, 20 to 75 °C), Test 2 (E2, 20 to 72 °C), and with an extended range, Test 3 (E3, −12 to 86 °C) and Test 4 (E4, −12 to 86 °C; repetition to assess reproducibility relative to E3), with 10 steady-state readings per setpoint. Erroneous readings were defined and removed (probe 3, Test 4), and set points without valid readings from probe 4 above 68 °C were excluded. Without data processing, the errors were consistent with the manufacturer’s stated ±0.5 °C, despite an inter-probe bias. Several correction models were evaluated (offset, affine linear, polynomial, and segmented); the probe-specific affine linear model provided the best overall compromise, reducing MAE (Mean Absolute Error) to 0.046 to 0.130 °C and RMSE (Root Mean Square Error) to 0.057 to 0.169 °C. The process uncertainty is dominated by the traceability of the Pt-100 probes and the effective nonuniformity of the isothermal volume, which limits the achievable accuracy. The results support the use of individually calibrated DS18B20 sensors for continuous monitoring, provided that the effective operating range is maintained. Full article

Buildings account for a substantial share of global energy demand, and decisions made during conceptual design strongly influence long-term operational consumption. This study presents an open, simulation-derived dataset to support early-stage estimation of residential energy use in a hot–arid context (New Cairo, Egypt). A parametric Rhino/Grasshopper workflow coupled with EnergyPlus was used to generate 12,000 annual simulations. The simulations were produced by systematically sampling key geometric, envelope, glazing, and operational variables, including building dimensions, orientation, window-to-wall ratio, envelope construction options, glazing properties, internal loads (lighting and equipment), and thermostat setpoints. For each case, annual end-use outputs (heating, cooling, lighting, and equipment energy) are reported alongside the corresponding input features, enabling design-space exploration, sensitivity analysis, and the development of surrogate and machine-learning models for rapid decision support. Verification checks and plausibility screening were applied to confirm successful simulation execution and consistent data extraction. In addition, dataset-level sampling diagnostics (marginal balance and correlation screening) are reported to support robust reuse in surrogate and machine-learning studies. The resulting dataset and documentation provide a reusable resource for researchers and practitioners investigating energy-informed residential design under hot-climate boundary conditions. Full article

The increasing demand for the energy-efficient and occupant-centred operation of educational buildings requires accurate and interpretable models capable of predicting indoor environmental conditions under real operating constraints. This study proposes a residual learning-enhanced grey-box modelling framework for predicting indoor air temperature and assessing indoor environmental quality indicators in a KNX-enabled educational building operating under simple thermostatic heating control. The approach combines a reduced-order discrete-time RC thermal model with a data-driven machine learning component trained to model the next-step residual between measured and simulated indoor temperatures. High-resolution KNX monitoring data were recorded at a 5 min sampling interval over three consecutive months (October–December) during the heating season. Using a chronological 70/30 train–test split, the identified RC grey-box model achieved a pooled test RMSE of 0.269 °C, an MAE of 0.126 °C, and an R² of 0.987. The proposed hybrid formulation achieved RMSE = 0.343 °C, MAE = 0.106 °C, and R² = 0.978 across 62,456 test samples. While the pooled RMSE remains influenced by occasional larger deviations in a small number of rooms, the hybrid model yields a consistent reduction in absolute error (≈16% MAE reduction) and reduced inter-room variability compared to the physics-based baseline. These results indicate that residual learning can enhance predictive robustness under decentralized thermostatic operation and limited sensing, while preserving physical interpretability. The proposed framework provides a practical and scalable solution for indoor temperature prediction and IEQ assessment in educational buildings using existing KNX automation data. Full article

Increased global energy consumption contributes to higher operational costs in the energy sector and results in environmental deterioration. This study evaluates the effectiveness of integrating Internet of Things (IoT) sensors and machine learning techniques to predict adaptive thermostat setpoints to support behavior-aware Heating, Ventilation, and Air Conditioning (HVAC) operation in residential buildings. The dataset was collected over two years from 2080 IoT devices installed in 370 zones in two buildings in Halifax, Canada. Specific categories of real-time information, including indoor and outdoor temperature, humidity, thermostat setpoints, and window/door status, shaped the dataset of the study. Data preprocessing included retrieving data from the MySQL database and converting the data into an analytical format suitable for visualization and processing. In the machine learning phase, deep learning (DL) was employed to predict adaptive threshold settings (“from” and “to”) for the thermostats, and a gradient boosted trees (GBT) approach was used to predict heating and cooling thresholds. Standard metrics (RMSE, MAE, and R²) were used to evaluate effective prediction for adaptive thermostat setpoints. A comparative analysis between GBT ”from” and “to” models and the deep learning (DL) model was performed to assess the accuracy of prediction. Deep learning achieved the highest performance, reducing the MAE value by about 9% in comparison to the strongest GBT model (1.12 vs. 1.23) and reaching an R² value of up to 0.60, indicating improved predictive accuracy under real-world building conditions. The results indicate that IoT-driven setpoint prediction provides a practical foundation for behavior-aware thermostat modeling and future adaptive HVAC control strategies in smart buildings. This study focuses on setpoint prediction under real operational conditions and does not evaluate automated HVAC control or assess actual energy savings. Full article

This study investigates the incorporation of a standardised flexibility protocol within a physics-based models to enable controllable demand-side flexibility in residential energy systems. A heating subsystem is developed using MATLAB/Simulink and Simscape, serving as a testbed for protocol-driven control within a Multi-Energy System (MES). A conventional thermostat controller is first established, followed by the implementation of an OpenADR event engine in Stateflow. Simulations conducted under consistent boundary conditions reveal that protocol-enabled control enhances system performance in several respects. It maintains a more stable and pronounced indoor–outdoor temperature differential, thereby improving thermal comfort. It also reduces fuel consumption by curtailing or shifting heat output during demand-response events, while remaining within acceptable comfort limits. Additionally, it improves operational stability by dampening high-frequency fluctuations in mdot_fuel. The resulting co-simulation pipeline offers a modular and reproducible framework for analysing the propagation of grid-level signals to device-level actions. The research contributes a simulation-ready architecture that couples standardised demand-response signalling with a physics-based MES model, alongside quantitative evidence that protocol-compliant actuation can deliver comfort-preserving flexibility in residential heating. The framework is readily extensible to other energy assets, such as cooling systems, electric vehicle charging, and combined heat and power (CHP), and is adaptable to additional protocols, thereby supporting future cross-vector investigations into digitally enabled energy flexibility. Full article

A one-dimensional analytical model for a proton exchange membrane fuel cell (PEMFC) engine is presented. The model is structured into three main subsystems: the fuel cell stack, the intake and exhaust system, and the thermal management system. The modeling of the thermal management system specifically encompasses key components such as the expansion tank, thermostat, pump, fan, and radiator. The heat transfer and fluid flow within key thermal management components—primarily fans and radiators—are analyzed via three-dimensional modeling. A porous media model represents the unit parallel-flow radiator, where the complex fin structures are replaced by a homogenized medium. This allows for the efficient calculation of 3D thermal and flow fields once the necessary constitutive parameters are identified. Ultimately, the one-dimensional (1D) thermal management system is coupled with the three-dimensional (3D) flow field analysis. This integrated 1D-3D co-simulation framework is implemented to enhance the computational fidelity of the PEMFC engine’s thermal management model. Full article

Smart thermostats are a key technology for reducing residential energy consumption in smart cities, but their real-world effectiveness depends on the interaction between automation, occupant behavior, and the design of behavioral interventions. This study presents a physics-informed assessment of thermostat strategies across Luxembourg’s single-family home stock, using an aggregate thermal model calibrated to eight years of hourly national heating demand and meteorological data. We simulate five categories of behavioral scenarios: dynamic thermostat adjustments, heat-wasting window-opening behavior, flexible comfort models, occupancy-based automation, and a portfolio of four probabilistic nudges (social comparison, real-time feedback, pre-commitment, and gamification). Results show that occupancy-based automation delivers the largest energy savings at 12.9%, by aligning heating with presence. In contrast, behavioral savings are highly fragile, as a stochastic window-opening behavior significantly erodes the 9.8% savings from eco-nudges, reducing the net gain to 7.6%. Among nudges, only social comparison yields significant savings, with a mean reduction of 7.6% (90% confidence interval: 5.3% to 9.8%), by durably lowering the thermal baseline. Real-time feedback and pre-commitment fail, achieving less than 0.5% savings, because they are misaligned with high-consumption periods. Thermal comfort, the psychological state of satisfaction with the thermal environment drives a large share of residential energy use. These findings demonstrate that effective smart thermostat design must prioritize robust, presence-responsive automation and interventions that reset default comfort norms, offering scalable, policy-ready pathways for residential energy reduction in urban energy systems. Full article

This paper describes the modeling, simulation, and experimental validation of a Hybrid supercapacitor–battery Energy Storage System (HESS) for photovoltaic (PV) modules under partial shading. The system is intended to provide an uninterruptible power supply for a DC primary load. The Hybrid Power System (HPS) architecture includes a DC/DC boost converter with a Maximum Power Point Tracking (MPPT) algorithm that optimizes photovoltaic (PV) energy extraction. Furthermore, two bidirectional DC–DC converters are dedicated to the battery and supercapacitor subsystems to allow the bidirectional power flow within the HPS. The proposed HESS is evaluated through MATLAB/Simulink simulations and experimentally validated on a prototype using real-time hardware based on the dSPACE DS1104. To optimize power flow within the HPS, two energy management strategies are implemented: the Thermostat-Based Method (TBM) and the Filter-Based Method (FBM). The results indicate that the thermostat-based strategy provides better battery protection under shading conditions. Indeed, with this approach, the battery can remain in standby for 300 s under total permanent shading (100%), and for up to 30 min under dynamic partial shading, thereby reducing battery stress and extending its lifetime. Full article

With the in-depth construction of the new power system, the importance of demand-side resources is becoming more and more prominent. The virtual power plant (VPP) has become a powerful means to explore the potential value of distributed resources. However, the differentiated resources between different VPPs are not reasonably deployed, and the problem of realizing the sharing of resources and the distribution of revenues among multi-VPP needs to be urgently solved. A cooperative operation optimization strategy for multi-VPP to participate in the energy and reserve capacity markets is proposed, and the potential risks associated with uncertainty in distributed generators (DGs) output are quantitatively assessed using conditional value-at-risk (CVaR). Firstly, due to the good adjustable performance of electric vehicles (EVs) and thermostatically controlled loads (TCLs), their virtual energy storage (VES) models are established to participate in VPP scheduling. Secondly, based on the asymmetric Nash negotiation theory, a P2P trading method between VPPs in a multi-marketed environment is proposed, which is decomposed into a virtual power plant alliance (VPPA) benefit maximization subproblem and a cooperative revenue distribution subproblem. The alternating direction multiplier method is chosen to solve the model, which protects the privacy of each subject. Simulation results show that the proposed multi-VPP cooperative operation optimization strategy can effectively quantify the uncertainty risk, maximize the alliance benefit, and reasonably allocate the cooperative benefit based on the contribution size of each VPP. Full article

The ongoing energy transition and decarbonization efforts have prompted the development of Hybrid Renewable Energy Systems (HRES) capable of integrating multiple generation and storage technologies to enhance energy autonomy. Among the available options, hydrogen has emerged as a versatile energy carrier, yet most studies have focused either on stationary applications or on mobility, seldom addressing their integration withing a single framework. In particular, the potential of Metal Hydride (MH) tanks remains largely underexplored in the context of sector coupling, where the same storage unit can simultaneously sustain household demand and provide in-house refueling for light-duty fuel-cell vehicles. This study presents the design and analysis of a residential-scale HRES that combines photovoltaic generation, a PEM electrolyzer, a lithium-ion battery and MH storage intended for direct integration with a fuel-cell electric microcar. A fully dynamic numerical model was developed to evaluate system interactions and quantify the conditions under which low-pressure MH tanks can be effectively integrated into HRES, with particular attention to thermal management and seasonal variability. Two simulation campaigns were carried out to provide both component-level and system-level insights. The first focused on thermal management during hydrogen absorption in the MH tank, comparing passive and active cooling strategies. Forced convection reduced absorption time by 44% compared to natural convection, while avoiding the additional energy demand associated with thermostatic baths. The second campaign assessed seasonal operation: even under winter irradiance conditions, the system ensured continuous household supply and enabled full recharge of two MH tanks every six days, in line with the hydrogen requirements of the light vehicle daily commuting profile. Battery support further reduced grid reliance, achieving a Grid Dependency Factor as low as 28.8% and enhancing system autonomy during cold periods. Full article

The El Niño–Southern Oscillation (ENSO) is the most consequential mode of interannual climate variability on the planet, yet its prediction has become complex due to the inability of classical paradigms to explain the observed co-evolution of the tropical mean state and interannual variability on decadal timescales. This article synthesizes the extensive research on ENSO rectification, exploring a paradigm that resolves this causality problem by recasting ENSO as an active architect of its own mean state. Tracing the intellectual development of this theory, starting from fundamental concepts such as the “dynamical thermostat” and “heat pump” hypotheses, modern analysis has identified the core physical mechanism as nonlinear dynamical heating (NDH), which is rooted in nonlinear heat advection during asymmetric ENSO cycles. The convergence of evidence from forced ocean models and observational diagnostics confirms a rectified signal characterized by an off-equatorial spatial pattern, providing a primary mechanism for tropical Pacific decadal variability (TPDV). By establishing a coherent framework linking high-frequency asymmetry with low-frequency variations, this review lays the foundation for future research and emphasizes the critical role of the rectification effect in improving decadal climate prediction. Full article

With the advancement of new power system construction, thermostatically controlled loads represented by regional air conditioning systems are being extensively integrated into the grid, leading to a surge in the number of user nodes. This large-scale integration of new loads creates challenges for the grid, as the resulting load data exhibits strong periodicity and randomness over time. These characteristics are influenced by factors like temperature and user behavior. At the same time, spatially adjacent nodes show similarities and clustering in electricity usage. This creates complex spatiotemporal coupling features. These complex spatiotemporal characteristics challenge traditional forecasting methods. Their high model complexity and numerous parameters often lead to overfitting or the curse of dimensionality, which hinders both prediction accuracy and efficiency. To address this issue, this paper proposes a load forecasting method based on spatiotemporal partitioning and collaborative cross-regional attention. First, a spatiotemporal similarity matrix is constructed using the Shape Dynamic Time Warping (ShapeDTW) algorithm and an adaptive Gaussian kernel function based on the Haversine distance. Spectral clustering combined with the Gap Statistic criterion is then applied to adaptively determine the optimal number of partitions, dividing all load nodes in the power grid into several sub-regions with homogeneous spatiotemporal characteristics. Second, for each sub-region, a local Spatiotemporal Graph Convolutional Network (STGCN) model is built. By integrating gated temporal convolution with spatial feature extraction, the model accurately captures the spatiotemporal evolution patterns within each sub-region. On this basis, a cross-regional attention mechanism is designed to dynamically learn the correlation weights among sub-regions, enabling collaborative fusion of global features. Finally, the proposed method is evaluated on a multi-node load dataset. The effectiveness of the approach is validated through comparative experiments and ablation studies (that is, by removing key components of the model to evaluate their contribution to the overall performance). Experimental results demonstrate that the proposed method achieves excellent performance in short-term load forecasting tasks across multiple nodes. Full article