Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems. (15th April 2022)

• Record Type: Journal Article
• Authors: Hamza Zafar, Muhammad; Mujeeb Khan, Noman; Mansoor, Majad; Feroz Mirza, Adeel; Kumayl Raza Moosavi, Syed; Sanfilippo, Filippo
• Is Part Of: Energy conversion and management. Volume 258(2022)
• Publication Date: 2022-04-15
• Subjects: Power forecasting -- Renewable energy resources (RES) -- Improved dynamic group based cooperative (IDGC); Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.115564
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A novel Dynamic Group based Cooperative Optimization Algorithm fused with Sine-Cosine Algorithm (IDGC) is proposed. An Improved Dynamic Group based Cooperative Optimization Algorithm (IDGC) is tested on Six standard functions. IDGC is used for hyper parameter tuning of RBFNN and GRNN. IDGC-RBFNN and IDGC-GRNN is used for PV/Wind Power forecasting. The proposed model has been tested on three different seasons, that is, winter, summer, and spring. Abstract: Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.