Modelling and optimization of a hybrid photovoltaic-parabolic trough concentrated solar power plant: Technical, economic, and environmental

Energy Nexus, Год журнала: 2025, Номер unknown, С. 100436 - 100436

Опубликована: Апрель 1, 2025

Язык: Английский

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A Novel EPSO Algorithm Based on Shifted Sigmoid Function Parameters for Maximizing the Energy Yield from Photovoltaic Arrays: An Experimental Investigation

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102967 - 102967

Опубликована: Сен. 1, 2024

Язык: Английский

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Optimized Nonlinear PID Control for Maximum Power Point Tracking in PV Systems Using Particle Swarm Optimization

Mathematical and Computational Applications, Год журнала: 2024, Номер 29(5), С. 88 - 88

Опубликована: Окт. 2, 2024

This paper proposes a high-performing, hybrid method for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The approach is based on an intelligent Nonlinear Discrete Proportional–Integral–Derivative (N-DPID) controller with the Perturb and Observe (P&O) method. feedback gains derived are optimized by metaheuristic algorithm called Particle Swarm Optimization (PSO). proposed methods appear to present adequate solutions overcome drawbacks of existing despite various weather conditions considered analysis, providing robust solution dynamic environmental conditions. results showed better performance accuracy compared those encountered literature. We also recall that this technique provides systematic design procedure search MPPT systems has not yet been documented literature best our knowledge.

Язык: Английский

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A Novel Approach for Predicting CO2 Emissions in the Building Industry Using a Hybrid Multi-Strategy Improved Particle Swarm Optimization–Long Short-Term Memory Model

Energies, Год журнала: 2024, Номер 17(17), С. 4379 - 4379

Опубликована: Сен. 1, 2024

The accurate prediction of carbon dioxide (CO2) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model predicting CO2 that combines multi-strategy improved particle swarm optimization (MSPSO) algorithm with long short-term memory (LSTM) model. Firstly, (PSO) is enhanced by combining tent chaotic mapping, mutation least-fit particles, random perturbation strategy. Subsequently, performance MSPSO evaluated using set 23 internationally recognized test functions. Finally, predictive MSPSO-LSTM assessed from Yangtze River Delta region as case study. results indicate coefficient determination (R2) reaches 0.9677, which more than 10% higher BP, LSTM, CNN non-hybrid models demonstrates significant advantages over PSO-LSTM, GWO-LSTM, WOA-LSTM models. Additionally, mean square error (MSE) 2445.6866 Mt, absolute (MAE) 4.1010 both significantly lower those Overall, high accuracy industry, offering robust development industry.

Язык: Английский

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Maximum power point tracking of solar photovoltaic under partial shading conditions based on improved salp swarm algorithm

Electric Power Systems Research, Год журнала: 2024, Номер 241, С. 111316 - 111316

Опубликована: Дек. 19, 2024

Язык: Английский

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