Search Results (1,461)

Protected agriculture increasingly requires solutions that reduce energy consumption and environmental impacts while maintaining stable microclimatic conditions. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) with solar technologies has emerged as a pathway toward autonomous and energy-efficient greenhouses and solar dryers. This study analyzes the scientific and technological evolution of this convergence using a mixed review approach bibliometric and systematic, following PRISMA 2020 guidelines. From Scopus records (2012–2025), 115 documents were screened and 79 met the inclusion criteria. Bibliometric results reveal accelerated growth since 2019, led by Engineering, Computer Science, and Energy, with China, India, Saudi Arabia, and the United Kingdom as dominant contributors. Thematic analysis identifies four major research fronts: (i) thermal modeling and energy efficiency, (ii) predictive control and microclimate automation, (iii) integration of photovoltaic–thermal (PV/T) systems and phase change materials (PCMs), and (iv) sustainability and agrivoltaics. Systematic evidence shows that AI, ML, and DL based models improve solar forecasting, microclimate regulation, and energy optimization; model predictive control (MPC), deep reinforcement learning (DRL), and energy management systems (EMS) enhance operational efficiency; and PV/T–PCM hybrids strengthen heat recovery and storage. Remaining gaps include long-term validation, metric standardization, and cross-context comparability. Overall, the field is advancing toward near-zero-energy greenhouses powered by Internet of Things (IoT), AI, and solar energy, enabling resilient, efficient, and decarbonized agro-energy systems. Full article

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

To improve the load frequency control (LFC) performance of power systems incorporating virtual power plants (VPPs) while reducing network resource consumption, a model predictive control (MPC) method based on a mixed time/event-triggered mechanism (MTETM) is proposed. This mechanism integrates an event-triggered mechanism (ETM) with a time-triggered mechanism (TTM), where ETM avoids unnecessary signal transmission and TTM ensures fundamental control performance. Subsequently, for the LFC system incorporating VPPs, a state hard constrained MPC problem is formulated and transformed into a “min-max” optimisation problem. Through linear matrix inequalities, the original optimisation problem is equivalently transformed into an auxiliary optimisation problem, with the optimal control law solved via rolling optimisation. Theoretical analysis demonstrates that the proposed auxiliary optimisation problem possesses recursive feasibility, whilst the closed-loop system satisfies input-to-state stability. Finally, validation through case studies of two regional power systems demonstrates that the MPC approach based on MTETM outperforms the ETM-based MPC approach in terms of control performance while maintaining a triggering rate of 33.3%. Compared with the TTM-based MPC algorithm, the MTETM-based MPC method reduces the triggering rate by 66.7%, while maintaining nearly equivalent control performance. Consequently, the results validate the effectiveness of the MTETM-based MPC approach in conserving network resources while maintaining control performance. Full article

This research presents a solution to the problem of controlling the energy demand and carbon footprint of old buildings, with the focus being on a (heated) building located in Madrid, Spain. A framework that incorporates AI and advanced hybrid ensemble approaches to make very accurate energy consumption predictions was developed and tested using the MATLAB environment. At first, the study evaluated six individual AI models (ANN, RF, XGBoost, RBF, Autoencoder, and Decision Tree) using a dataset of 100 points that were collected from the building’s sensors. Their performance was evaluated with high-quality data, which were ensured to be free of missing values or outliers, and they were prepared using L1/L2 normalization to guarantee optimal model performance. Later, higher accuracy was achieved through combining the models by means of hybrid ensemble techniques (voting, stacking, and blending). The main contribution is the application of a Long Short-Term Memory (LSTM) model for predicting the energy consumption of the building and, very importantly, its carbon footprint over a 30-year period until 2050. Additionally, the proposed methodology provides a structured pathway for existing buildings to progress toward nearly Zero-Energy Building (nZEB) performance by enabling more effective control of their energy demand and operational emissions. The comprehensive assessment of predictive models definitively concludes that the blended ensemble method is the most powerful and accurate forecasting tool, achieving 97% accuracy. A scenario where building heating energy use jumps to 135 by 2050 (a 35% increase above 2020 levels) represents an alarming complete failure to achieve energy efficiency and decarbonization goals, which would fundamentally jeopardize climate targets, energy security, and consumer expenditure. Full article

Improving financial time series forecasting presents challenges because models often struggle to identify diverse fault patterns in unseen data. This issue is critical in fintech, where accurate and reliable forecasting of financial data is essential for effective risk management and informed investment strategies. This work addresses these challenges by initializing the weights and biases of two proposed models, Gated Recurrent Units (GRUs) and the Echo State Network (ESN), with different chaotic sequences to enhance prediction accuracy and capabilities. We compare reservoir computing (RC) and recurrent neural network (RNN) models with and without the integration of chaotic systems, utilizing standard initialization. The models are validated on six different datasets, including the 500 largest publicly traded companies in the US (S&P500), the Irish Stock Exchange Quotient (ISEQ) dataset, the XAU and USD forex pair (XAU/USD), the USD and JPY forex pair with respect to the currency exchange rate (USD/JPY), Chinese daily stock prices, and the top 100 index of UK companies (FTSE 100). The ESN model, combined with the Lorenz system, achieves the lowest error among other models, reinforcing the effectiveness of chaos-trained models for prediction. The proposed ESN model, accelerated by the Kintex-Ultrascale KCU105 FPGA board, achieves a maximum frequency of $83.5$ MHz and a power consumption of $0.677$ W. The results of the hardware simulation align with MATLAB fixed-point analysis. Full article

As the global context for sustainable actions increases continuously, understanding the psychological and demographic factors that influence green purchase intention (GPI) is vital for promoting sustainable consumer behavior. This study addresses the gap in the literature regarding how age affects sustainability consciousness (SC) and then influences GPI. The study employs a multidimensional construct measuring perception of people’s attitudes, knowledge, and behavior with respect to the economic, social, and environmental domain. The purpose of the study was to examine the direct and indirect effects of age on GPI through the mediators of sustainability knowledge (SKNOW), sustainability attitude (SATT), and sustainable behavior (SBEH). A serial mediation model (Model 6) developed by Hayes was applied using the PROCESS macro in SPSS version 26. Data were collected from a general adult population with purchasing power who independently make purchasing decisions in their household from Varazdin County, located in the northern part of the Republic of Croatia, representing different age groups and analyzed to test the hypothesis. In total 323 respondents participated. Results revealed that age had no direct effect on GPI, but significant indirect effects were found through the serial mediation. Specifically, the older groups showed stronger sustainability behavior, which significantly predicted GPI. The findings support the multidimensional structure of SC and highlight the importance of educational and behavioral strategies in promoting sustainable consumption, particularly tailored to specific age groups. This research contributes to sustainability and consumer behavior literature by demonstrating how age influences green purchase intention through serial mediation pathways. Full article

Rising energy demand, fossil fuel depletion, and global warming are accelerating research into sustainable energy solutions, with growing interest in hydrogen as a promising alternative. This research presents a detailed experimental investigation and novel digital twin (DT) models for an integrated hydrogen-based energy system consisting of an Anion Exchange Membrane Electrolyzer (AEMEL), Proton Exchange Membrane Fuel Cell (PEMFC), hydrogen storage, and Battery Energy Storage System (BESS). Conducted at a real-world facility in Risavika, Norway, the study employed commercial units: the Enapter EL 4.1 AEM electrolyzer and Intelligent Energy IE-Lift 1T/1U PEMFC. Experimental tests under dynamic load conditions demonstrated stable operation, achieving hydrogen production rates of up to 512 NL/h and a specific power consumption of 4.2 kWh/Nm³, surpassing the manufacturer’s specifications. The PEMFC exhibited a unique cyclic operational mechanism addressing cathode water flooding, a critical issue in fuel cell systems, achieving steady-state efficiencies around 43.6% under prolonged (190 min) rated-power operation. Subsequently, advanced DT models were developed for both devices: a physics-informed interpolation model for the AEMEL, selected due to its linear and steady operational behavior, and an ANN-based model for the PEMFC to capture its inherently nonlinear, dynamically fluctuating characteristics. Both models were validated, showing excellent predictive accuracy (<3.8% deviation). The DTs integrated manufacturer constraints, accurately modeling transient behaviors, safety logic, and operational efficiency. The round-trip efficiency of the integrated system was calculated (~27%), highlighting the inherent efficiency trade-offs for autonomous hydrogen-based energy storage. This research significantly advances our understanding of integrated H₂ systems, providing robust DT frameworks for predictive diagnostics, operational optimization, and performance analysis, supporting the broader deployment and management of hydrogen technologies. Full article

Heating, ventilation, and air conditioning (HVAC) systems are the primary energy consumers in modern office buildings, with chillers consuming the most energy. As critical components of building air conditioning, the effective functioning of HVAC systems holds substantial importance for energy preservation and emission mitigation. To enhance the operational performance of HVAC systems and accomplish energy conservation objectives, precise cooling load forecasting is essential. This research employs an office facility in Binzhou City, Shandong Province, as a case investigation and presents a day-ahead scheduling-based model predictive control (MPC) approach for HVAC systems, which targets minimizing the overall system power utilization. An attention mechanism-based long short-term memory (LSTM) neural network forecasting model is developed to predict the building’s cooling demand for the subsequent 24 h. Based on the forecasting outcomes, the MPC controller adopts the supply–demand equilibrium between cooling capacity and cooling demand as the central constraint and utilizes the particle swarm optimization (PSO) algorithm for rolling optimization to establish the optimal configuration approach for the chiller flow rate and temperature, thereby realizing the dynamic control of the HVAC system. To verify the efficacy of this approach, simulation analysis was performed using the TRNSYS simulation platform founded on the actual operational data and meteorological parameters of the building. The findings indicate that compared with the conventional proportional–integral–derivative (PID) control approach, the proposed day-ahead scheduling-based MPC strategy can attain an average energy conservation rate of 9.23% over a one-week operational period and achieve an energy-saving rate of 8.25% over a one-month period, demonstrating its notable advantages in diminishing building energy consumption. Full article

Satellite battery status prediction is crucial for ensuring the healthy operation of future satellite constellations. However, traditional telemetry-based methods, where satellite battery status is transmitted in real time to ground stations for processing, consume significant satellite bandwidth and introduce response delays. Advances in onboard computing and federated learning (FL) enable local model training and centralized parameter aggregation, reducing transmission overhead while leveraging distributed satellite data. Nevertheless, the unique orbital motion of satellites presents challenges for FL, primarily due to battery status heterogeneity arising from varying sunlight exposure. Limited onboard energy further necessitates balancing model performance with battery efficiency during local training. To tackle these issues, we propose ObsBattery—a position-aware FL framework that clusters satellites based on their orbital positions to improve model accuracy. ObsBattery employs a Dueling Deep Q-Network to dynamically determine satellite clustering and adapt local training rounds according to power availability, thereby reducing energy consumption during low-power phases. Evaluations on a real-world satellite battery dataset show that ObsBattery significantly improves both prediction accuracy and energy efficiency. Compared to a standard clustered FL approach, it reduces model MAE by 16% and energy consumption ratio by 6% under experimental conditions. Full article

This paper proposes a hybrid machine learning framework for detecting electricity fraud within the broader context of Non-Technical Losses (NTLs) in power-distribution systems. The framework combines unsupervised anomaly detection using Isolation Forest with supervised classification through XGBoost, exploiting the complementary strengths of both algorithms. Using real consumption data from a Peruvian utility, the approach integrates domain-informed feature engineering to capture behavioral, temporal, and contextual indicators of irregular usage. To address the extreme class imbalance inherent to fraud datasets, the SMOTETomek hybrid resampling technique was applied, enhancing minority-class representation and decision boundary clarity. Experimental results achieved high predictive performance on the test set (AUC-ROC = 0.999, F1-score = 0.77) using an optimized decision threshold of 0.6. Moreover, SHAP-based interpretability analysis identified extreme monthly variations, prolonged low-consumption periods, and tariff category as key behavioral predictors of fraudulent activity. The robustness of the proposed framework was further validated through a 5-fold cross-validation procedure during the training phase, ensuring consistent performance across different data partitions. Overall, the proposed framework demonstrates not only robust and explainable performance but also practical operational value, providing utilities with a scalable data-driven tool to optimize inspection strategies and maximize recovery of non-technical losses. Full article

Accurate short-term prediction of residential power consumption is imperative for efficient energy system management. However, the complexity of high-resolution load data, nonlinear dynamics of load fluctuation, and external factor interactions pose challenges to traditional load forecasting methods. This work introduces a diffusion model-based and attention mechanism-enhanced temporal forecasting framework to address the volatility and uncertainty in load patterns. The proposed model enhances the noise robustness via diffusion processes, captures multi-scale temporal features through temporal convolutional networks, and adaptively focuses on critical time steps using attention mechanisms. Further, a dynamically weighted loss function is designed to improve both the prediction accuracy and latent representation quality. Experiments on multiple real-world residential load datasets show that the proposed model always outperforms benchmarks, reducing on average the mean absolute error (MAE) by 47.4%, symmetric mean absolute percentage error (SMAPE) by 39.7%, and mean absolute percentage error (MAPE) by 57.6%. It also achieves the superior root mean square error (RMSE) and Pearson correlation coefficient (PCC) performance, validating its effectiveness for high-resolution and multi-modal load forecasting. Full article

Current research on the performance and emissions of vehicles and internal combustion engines should include analysis of efficiency-enhancing technologies and emission reduction strategies across a variety of vehicle systems. To improve both performance and emission control, it is necessary to examine advanced heavy-duty driveline technologies, considering their real-world impact on fuel economy and emission reduction under various driving conditions. This article will deal with predictive cruise control (PCC) and its influence on the operating characteristics of a truck, specifically a semi-trailer combination. The measurement was carried out using dynamic driving tests of a truck on a selected road. The use of electronic systems for automatically maintaining the vehicle’s motion states (especially speed) based on the specified conditions most often has several benefits for the driver not only from the point of view of vehicle operation but also from the point of view of transport companies (cost reduction). It is generally known that the use of these electronic systems reduces the vehicle’s fuel consumption and therefore also reduces the amount of exhaust gases. Comparing the individual directions of the road tests, the difference in relative maximum power utilization between the driver and the PCC system was 26.42% in the ST-MY direction and 23.81% in the MY-ST direction. The use of PCC also results in fuel savings of up to 17.11%. This study provides new insights into the quantification of the impact of PCC on fuel consumption in real operating conditions and highlights the potential for integrating PCC into driver assistance systems and logistics planning to reduce costs and emissions in freight transport. Further research could focus on applying this system in specific road conditions. Full article

This paper investigates the predictability of stock returns in the Chinese market through the lens of consumption–wealth dynamics within a broader financial system. We focus on two key state variables derived from modern consumption-based asset pricing models: the ratio of log surplus consumption (scr), from the habit-formation framework, and the log consumption–wealth ratio (cay), from the long-run cointegration framework. Using quarterly data from the CSI 300 index between 2012Q1 and 2018Q4, our system-based analysis reveals a horizon-dependent pattern of predictability. The results show that scr is a strong short-term predictor of excess stock returns, reflecting cyclical changes in risk aversion, whereas cay demonstrates superior predictive power over mid- to long-term horizons, consistent with its role as a proxy for long-run expectations. Interestingly, combining scr and cay does not improve predictive performance, suggesting that the economic mechanisms they capture are distinct rather than complementary in the Chinese market. These findings provide evidence on how interconnected macro-financial variables shape stock return dynamics, highlighting the importance of considering temporal horizons when modeling financial systems. Full article

Effectively forecasting electricity generation, consumption, and pricing enhances power utilization efficiency, safeguards the stable operation of power systems, and assists power generation enterprises in formulating rational generation plans and dispatch schedules. The electricity generation, consumption, and pricing system exhibits complex chaotic dynamics. Establishing effective predictive models by leveraging the strong coupling and multi-scale uncertainty characteristics of nonlinear dynamical systems is a key challenge in grey modelling. This study leverages grey differential information to effectively transform differential equations into difference equations. Fractional-order cumulative generation operations enable more refined extraction of data characteristics. Based on the coupling and uncertainty features of electricity generation–consumption–pricing dynamics within complex power systems, three types of fractional-order multivariate grey models are established. These models both reflect the system’s dynamic relationships and expand the conceptual framework for grey prediction modelling. Simultaneously, the effectiveness of these three models is analyzed using data on generation, consumption, and prices from both new and traditional power sources within China’s electricity system. Employing identical annual data, the models are evaluated from two distinct perspectives: variations in the numbers of simulated and predicted variables. Experimental results demonstrate that all three novel models perform well. Finally, the most effective predictive application of the three models was selected to forecast electricity generation, consumption, and pricing in China. This provides a basis for China’s power system and supports national macro-level intelligent energy dispatch planning. Full article

Telecommunication Base Transceiver Stations (BTSs) require a resilient and sustainable power supply to ensure uninterrupted operation, particularly during grid outages. Thus, this paper proposes an Adaptive Model Predictive Control (AMPC)-based Energy Management System (EMS) designed to optimize energy dispatch and demand response for a BTS powered by a renewable-based microgrid. The EMS operates under two distinct scenarios: (a) non-grid outages, where the objective is to minimize grid consumption, and (b) outage management, aiming to maximize BTS operational time during grid failures. The system incorporates a dynamic weighting mechanism in the objective function, which adjusts based on real-time power production, consumption, battery state of charge, grid availability, and load satisfaction. Additionally, a demand response strategy is implemented, allowing the BTS to adapt its power consumption according to energy availability. The proposed EMS is evaluated based on BTS loss of transmitted data under different renewable energy profiles. Under normal operation, the EMS is assessed regarding grid energy consumption. Simulation results demonstrate that the proposed AMPC-based EMS enhances BTS resilience. Full article