Towards farm-level net-zero greenhouse gas emissions: Contributions of climate mitigation actions - A study of four European crop and dairy farms.

The global energy crisis presents itself as an ongoing problem which photovoltaic (PV) panels address effectively by converting renewable solar power into electricity. The performance of PV panels suffers from operating temperature increases causing important decreases in electrical efficiency. A reliable cooling system needs implementation to preserve thermal stability along with maximizing energy conversion performance. A laboratory investigation evaluates the implementation of distilled thermoelectric heat sinks aimed at reducing PV panel surface temperatures for better overall performance enhancement. The laboratory experiment used closed-loop cooling with parallel-installed thermoelectric modules below and above PV panels to measure various configuration performances under controlled indoor testing. Tests took place at the college of technical engineering at University of Thi-Qar to identify the best thermal energy (TE) module layout which produced the minimum achievable base temperature of the PV panel. The study conducted a systematic analysis of different cooling setup configurations which helped identify the top performing design that simultaneously reduced energy losses and achieved maximum power output. The study’s findings show that proper positioning of TE modules creates substantial improvements for PV system thermal regulation. The best arrangement yielded substantial temperature reduction alongside enhanced energy efficiency which demonstrated TE-assisted cooling can be an effective solution for future solar power systems.

Deep learning prediction models for short-term solar photovoltaic power generation forecasting

Networked pitch-derived semicoke-coated Ni foam with synergistic photon and fluidic management for highly efficient solar steam generation

Feasibility study of solar power curtailment prediction using multivariate dynamic time warping – based machine learning: A case study of Jeju island

Research on off-grid off-design scheduling strategies for a supercritical CO2 concentrating solar power cogeneration system

Techno-economic and environmental assessment of photobioreactors coupled with solar power for Chlorella vulgaris cultivation

High-performance composite molten salt thermal energy storage material based on resources recovery from waste salt for concentrated solar power applications

Concrete-based thermal energy storage (CTES) for concentrated solar power plants and built environment

Repurposing spent battery waste into plasmonic photothermal membrane for efficient solar-driven evaporation and freshwater production.

Single-atom photocatalysts have rapidly emerged as a frontier in photocatalysis, offering complete atom utilization and exceptional catalytic activity while enhancing light harvesting, charge separation, and surface reaction kinetics. This perspective summarizes recent progress in surface and interface engineering of single-atom photocatalysts, achieved through strategies such as interfacial modulation, defect engineering, heteroatom doping, and related approaches. In particular, we discuss electronic interactions of single atoms on various supports, their coordination environment, and electronic structure engineering of single atoms in governing the photocatalytic hydrogen evolution reaction (HER). By highlighting recent advancements and discussing key challenges alongside potential strategies, this work aims to provide clear design principles that will guide future research on single-atom catalysts for photocatalytic energy conversion. This study supports the global transition toward clean and sustainable energy technologies, aligning with the United Nations Sustainable Development Goals (SDGs).

Short-term forecasting of solar and wind power generation is critical for smart grid management but challenging due to non-stationarity and extreme generation events. This study addresses a multi-task learning problem: regression-based forecasting of power output and binary detection of extreme events defined by a quantile-based threshold (q = 0.90). A hybrid spatio-temporal model, DP-STH++, is proposed, implementing parallel causal fusion of LSTM, GRU, a causal Conv1D stack, and a lightweight causal transformer. The architecture employs regression and classification heads, while an uncertainty-weighted mechanism stabilizes multitask optimization in the regression tasks; extreme event detection performance is evaluated using AUC. Training and evaluation follow a leakage-safe protocol with chronological data processing, calendar feature integration, time-aware splitting, and training-only estimation of scaling parameters and extreme thresholds. Experimental results obtained with a one-hour forecasting horizon and a 24 h context window demonstrate that DP-STH++ achieves the best regression performance on the hold-out set (RMSE = 257.18, MAE = 174.86–287.90, MASE = 0.2438, R2 = 0.9440) and the highest extreme event detection accuracy (AUC = 0.9896), ranking 1st among all compared architectures. In time-series cross-validation, the model retains the leading position with a mean MASE = 0.3883 and AUC = 0.9709. The advantages are particularly pronounced for wind power forecasting, where DP-STH++ simultaneously minimizes regression errors and maximizes AUC = 0.9880–0.9908.

Deep learning models often encounter two key challenges in developing intelligent and scalable forecasting frameworks for renewable energy systems: input feature space dimensionality and sensitivity to hyperparameter settings. These limitations increase computational cost and compromise generalization and robustness. This paper presents a hybrid deep learning-optimization framework that leverages cognitively inspired metaheuristics to address these challenges, employing the Binary iHow Optimization Algorithm (biHOW) for feature selection and its continuous counterpart, iHOW, for hyperparameter tuning. Both variants emulate human cognitive phases-data absorption, information analysis, reinstitution, and adaptive knowledge development enabling efficient traversal of complex search spaces. Using the Multi-Scale Attention Network (MSAN) as the forecasting backbone, which is well suited for modeling renewable energy time series due to its ability to capture multi-scale temporal dependencies ranging from short-term fluctuations to long-term seasonal patterns, the proposed framework achieved high accuracy for wind and solar generation prediction. The MSAN model attained Mean Squared Errors (MSE) of 0.0105 for wind and 0.0976 for solar forecasting. Applying biHOW for feature selection reduced the average misclassification rate to 0.3925 (wind) and 0.4161 (solar) while identifying compact, interpretable feature subsets. The iHOW optimizer further fine-tuned architectural and training parameters, decreasing MSE to [Formula: see text] for wind and [Formula: see text] for solar, outperforming state-of-the-art metaheuristics including HHO, GWO, PSO, and JAYA. These findings demonstrate the effectiveness of iHOW-based optimization in enhancing forecasting accuracy and computational scalability. The proposed hybrid framework supports adaptive forecasting for intelligent energy management within modern smart grids.

Abstract Driven by rising global energy demand and environmental concerns over fossil fuels, photocatalytic water splitting for solar-driven hydrogen production has gained prominence. Herein, we propose a novel GeC/B 2 SSe van der Waals (vdW) heterostructure. Employing the quantum ESPRESSO code based on density functional theory (DFT), combined with the HSE06 hybrid functional and DFT-D3 van der Waals correction, we conduct a comprehensive evaluation of its structural, electronic, optical, and transport properties, alongside its strain-tunable photocatalytic behaviour. The heterostructure exhibits only 3.75% lattice mismatch and is stabilized by weak vdW interactions, ensuring energetic, dynamic, and thermodynamic stability at room temperature. It is an indirect bandgap semiconductor (2.64 eV) with a Type-II band alignment, enabling spatial charge separation. A built-in interfacial electric field (2.47 eV) further suppresses electron–hole recombination. The band edges straddle the water redox potentials across the entire pH range of 1–14, fulfilling the thermodynamic criteria for universal overall water splitting. The system shows strong visible-light absorption with a peak absorption coefficient of 1.04 × 10 5 cm −1 at 3.03 eV and ultrahigh hole mobility (69276.91 cm 2 V −1 s −1 along the armchair direction), with mobility asymmetry between carriers reducing recombination. Under biaxial strain (–6% to +6%), the redox alignment is preserved, and tensile strains (+4% to +6%) notably enhance light absorption. These results establish GeC/B 2 SSe as a highly promising photocatalyst for solar hydrogen generation, offering a solid theoretical basis for future experimental development.

Agricultural greenhouses are usually located in the suburbs or remote areas away from towns, and generally speaking, the cost of transmission and power supply is high, and some remote areas do not even have electricity supply. However, traditional greenhouses contain many different electrical equipment and facilities, and a stable power supply is essential for the normal, economical and efficient operation of the greenhouse. Modern agricultural greenhouses also need to be equipped with complete lighting systems, temperature and humidity control systems, ventilation systems, carbon dioxide concentration controlsystems, irrigation sprinkler systems, etc., which are difficult for traditional greenhouses to achievesmoothly. Traditional greenhouses are usually covered with plastic film, which usually needs to be replaced every year, and the discarded plastic film does not meet the requirements of energy conservation and environmental protection. The problem of "thermal insulation" in greenhouses has also been plaguing greenhouse growers. From the perspective of planting cycle, traditional greenhouses are generally onlyplanted twice a year, and the economic benefits are limited.

This paper explores the hybridization of Concentrated Solar Power (CSP) plants with wind and photovoltaic (PV) technologies to meet self-consumption demands in two distinct locations: Calama (Chile) and San José del Valle (Spain). We evaluate various hybrid configurations under two primary scenarios: one where excess energy cannot be utilized or fed into the grid, and another where electric heaters are used to transfer excess energy to thermal storage systems. Our findings reveal that hybrid plants significantly reduce the LCOE and increase the Capacity Factor (CF) compared to standalone CSP plants, with optimal configurations varying by location due to differences in climatic conditions. In Calama, wind energy plays a crucial role, whereas in San José del Valle, a combination of PV and wind is more effective. Additionally, the use of electric heaters to store surplus energy enhances system efficiency. This study underscores the viability and efficiency of hybrid CSP plants, especially when adapting to regional climatic and geographic characteristics, providing a compelling case for their implementation in diverse locations.

This paper presents a case study of a Home Energy Management System (HEMS) integrating photovoltaic (PV) generation, battery energy storage (BES), thermal storage, and a heat pump in a single-family household operating under a dynamic electricity tariff. The analysis is based on real operational data and focuses on system performance under varying solar generation conditions. The results show that during sunny days, the battery storage absorbs the entire surplus PV generation until reaching full capacity, i.e., 10 kWh, effectively preventing curtailment and maximizing self-consumption. On days with limited solar production, the system actively utilizes the available storage capacity by shifting energy use in time and, when economically justified, temporarily charging the battery from the grid during low-price periods. This strategy reduces electricity purchases during peak-price hours and stabilizes household energy costs. For the analyzed case, daily PV generation self-consumption exceeded 70% on high-generation days, while the application of storage-based load shifting under dynamic tariffs reduced daily electricity costs by up to 30% compared to a fixed-rate tariff. The study confirms that the economic and operational performance of residential energy systems under dynamic pricing depends primarily on adaptive storage control rather than on PV capacity alone, highlighting the central role of battery energy storage in year-round energy optimization.

An improved Sinh Cosh optimizer (ISCHO) is proposed to resolve load frequency control (LFC) tasks. The original Sinh Cosh optimizer (SCHO) employs a fixed iteration-based switching function to balance exploration and exploitation, which lacks awareness of search dynamics and leads to inefficient optimization. Therefore, this paper proposes a “first grabbing then washing” strategy to dynamically balance exploration and development. The proposed ISCHO technique is tested on 13 benchmark functions and compared with Particle Swarm Optimization, Sine Cosine Algorithm, and Grey Wolf Optimizer, demonstrating superior optimization performance. Furthermore, a new controller based on the two-degree-of freedom PID controller (2DOF-PID), the two-degree-of freedom with double integral feedback PID controller (2DOF-PIDF-II), is proposed. A two-area multi-source interconnected power system, incorporating thermal, hydraulic, wind, and solar generation units with nonlinearities (GRC and GDB), uncertainties, and load fluctuations, is employed to validate the proposed approach. Quantitative results under step load perturbation demonstrate that the ISCHO-optimized 2DOF-PIDF-II controller significantly outperforms other methods. For area 1 frequency deviation, ISCHO reduces the maximum overshoot by 38.37%, 19.09%, and 21.48% compared to PSO, SCA, and SCHO. For tie-line power deviation, maximum overshoot is reduced by 53.00% compared to PSO. These results confirm that the proposed ISCHO-tuned 2DOF-PIDF-II controller substantially enhances system frequency stability under various operating conditions.

A series of two-dimensional superlattice structures designed from polyoxometalates for efficient solar steam generation

Integrating large language models into Peer-to-Peer energy management for multi-tenant buildings: A guardrail approach to ensuring resilience.