Enhancing photovoltaic (PV) systems’ energy extraction efficiency is a critical challenge, particularly due to the degraded performance of existing MPPT methods under complex partial shading and dynamic irradiance conditions. In this work, a new maximum power point tracking controller based on improved artificial ecosystem-based optimization and the perturb and observe algorithm is proposed. The key modifications involve restructuring the operational phases of the original algorithm, starting with early exploitation followed by guided exploration, while eliminating the decomposition phase, resulting in faster convergence, reduced oscillations, lower computational complexity, and fewer control parameters. The enhanced artificial ecosystem’s global search capabilities and the P O algorithm’s local fine-tuning capability guarantee quick and precise tracking of the global maximum power (GMPP). The efficiency of the proposed approach is evaluated under different scenarios and 12 different duty cycle scenarios to address sensitivity of metaheuristic-based MPPT techniques to initial conditions, ensuring accurate GMPP tracking regardless of starting points. A comparative study is conducted using several population-based optimization algorithms. The obtained results demonstrate that the proposed algorithm achieves a tracking efficiency of up to 99.99
Forecasting building Heating/Cooling demand is essential for designing low-carbon, energy-efficient, and resilient buildings. Yet, it remains challenging to select the most accurate forecast model. Efficient evaluation and understanding of how the building envelope affects performance metrics depend on this selection. The present study investigates three different forecast models and their hybridization. These include a statistical SARIMA (Seasonal Autoregressive Integrated Moving Average) model, a deep learning LSTM (Long Short-Term Memory) model, and the decomposable Prophet time series model. A selected hybrid forecasting model is also examined for predicting energy demand. The proposed models were applied to a real monthly energy consumption dataset for different residential buildings, namely A, B, C, and D, each with 80 m2 area, active/passive factors, and operational and climatic conditions, with the only difference being the thermophysical properties of their envelopes. Based on the results, the SARIMA model shows the highest precision for heating energy forecasting, with the lowest scattering index (SI) of 0.03; otherwise, the hybrid SARIMA-Prophet model achieves the highest accuracy for cooling energy forecasting, with an SI of 0.18. The ten year forecasted scenarios are applied to evaluate the long-term impact of the envelope’s thermophysical properties on buildings’ energy efficiency, CO2 emissions, and operating costs. Compared to the reference building (Building A), which is insulated only by clay brick wall insulation, the building insulated by an airgap layer (Building D) demonstrates a 27
This paper examines the integration of hydrogen storage in renewable-intensive energy systems through the application of stochastic optimization processes, followed by a comprehensive sensitivity analysis. Our findings indicate that while current hydrogen storage technology is costly and inefficient, reducing hydrogen costs to 12.5
Hydropower is able to provide flexibility for the energy system by saving water in a reservoir and, in some stations, also the possibility of pumping. However, modeling hydroelectric systems can be challenging due to the complexity of cascade river systems and data limitations. Energy system models are therefore often using a simplified approach called a "hydropower equivalent." Calculating an equivalent involves bi-level optimization that minimizes the differences between a detailed reference model and the simplified system. Previous research has demonstrated the flexibility and effectiveness of applying Particle Swarm Optimization (PSO) to solve bi-level optimization problems. However, its performance has not been compared to other search algorithms, such as Bayesian optimization and random sampling. This study examines the robustness of PSO solutions by comparing them with those of other methods. The Optuna framework is used in this research because it offers implementations of both random sampling and Bayesian optimization. Additionally, it provides a functional ANOVA (Analysis of Variance) to calculate the importance of individual variables, which enhances understanding of the solutions and highlights the significance of each variable. Furthermore, a new combined approach of Bayesian optimization and PSO is proposed. While PSO tends to be more accurate for complex equivalent systems with many parameters, Optuna’s framework provides significant advantages in parameter analysis. A combined approach employing both methods showed promising results for optimizing more complex models. Overall, Optuna enhances the understanding of each parameter’s impact, offering valuable insights for designing equivalent hydropower systems.
Accurate yet interpretable inflow forecasts are essential for hydropower operation. This study introduces modular Hydrologiska Byråns Vattenbalansavdelning-deep learning (mHBV-DL), a physics-guided hybrid deep learning framework that integrates the HBV model structure with neural networks, exposing intermediate hydrological states and fluxes alongside inflow predictions to enhance physical explainability. To systematically evaluate physics-DL integration strategies, four model configurations are constructed and compared, balancing learning flexibility with physical consistency. The best-performing mHBV-DL configuration achieved a Nash–Sutcliffe efficiency coefficient of 0.833, outperforming the standalone HBV model and remaining competitive with the architecture-matched black-box DL baselines. Its intermediate outputs also reflected expected hydrological patterns. The modular architecture and intermediate state and flux tracking enable systematic analysis of seasonal performance and model limitations. These findings establish mHBV-DL as an effective framework for combining predictive performance with physical explainability in catchment inflow modelling and provide a basis for future lead-time-based forecasting applications.
