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

Autor: Alper Kerem, Ali Saygin, Rasoul Rahmani
Rok vydání: 2021
Předmět:
Zdroj: Environmental Science and Pollution Research. 29:50998-51010
ISSN: 1614-7499
0944-1344
Popis: Wind is a stochastic and intermittent renewable energy source. Due to it’s nature, it is extremely hard to forecasting of wind power. Accurate wind power forecasting can be encouraging and motivating for investors to shed light on future uncertainties caused by global warming. Thus, CO2 and other greenhouse gases 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, it has been carried out wind power forecasting studies with high accuracy despite the height differences between the sensors. That is, for WMS-1 and WMS-2, it has succeeded the wind power forecasting at 61m and 60.3m using temperature (3m), humidity (3m), and pressure (3.5m) data. The performance results were presented in tables and graphs.
Databáze: OpenAIRE
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