A green energy research: forecasting of wind power for a cleaner environment using robust hybrid metaheuristic model

• Published in: Environmental science and pollution research international Vol. 29; no. 34; pp. 50998 - 51010
• Main Authors: Kerem, Alper, Saygin, Ali, Rahmani, Rasoul
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Alternative energy sources; Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Carbon dioxide; Clean energy; Climate change; Earth and Environmental Science; Ecotoxicology; electric power; Energy; Energy research; Environment; Environmental Chemistry; Environmental Health; Environmental science; Forecasting; Global warming; Green energy; Green Energy for Environmental Sustainability; Greenhouse effect; Greenhouse gases; greenhouses; Heuristic methods; humidity; hybrids; Mathematical models; Neural networks; Particle swarm optimization; Renewable energy sources; Robustness (mathematics); temperature; Waste Water Technology; Water Management; Water Pollution Control; Weather forecasting; Weather stations; Wind measurement; Wind power; Wind speed; Green energy; Renewable energy; Wind power forecasting; Particle swarm optimization (PSO); Sustainability; Hybrid metaheuristic model; Artificial neural network (ANN); Radial movement optimization (RMO)
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-021-16494-7

Wind is a stochastic and intermittent renewable energy source. Due to its nature, it is extremely hard to forecast wind power. Accurate wind power forecasting can be encouraging and motivating for investors to shed light on future uncertainties caused by global warming. Thus, CO 2 and other greenhouse gases (GHG) which are harmful to the environment will not be released into the atmosphere, while generating electrical energy. This paper presents a novel precise, fast and powerful hybrid metaheuristic wind power forecasting approach based on statistical and mathematical data from real weather stations. The model was developed as a hybrid metaheuristic algorithm based on artificial neural networks (ANNs), particle swarm optimization (PSO) and radial movement optimization (RMO). Real-time wind data was gathered from wind measuring stations (WMS) at two separate places in Burdur and Osmaniye cities, Turkey. The key contribution of this new model is the ability to perform wind power forecasting studies, without needing wind speed data, with high accuracy and rapid solutions. Also, wind power forecasting studies with high accuracy have been carried out despite the height differences between the sensors. That is, for WMS-1 and WMS-2, it has succeeded the wind power forecasting at 61 m and 60.3 m using temperature (3 m), humidity (3 m) and pressure (3.5 m) data. The performance results were presented in tables and graphs.