Muhammad Munsif1,2, Fath U Min Ullah1,2, Samee Ullah Khan1,2, Noman Khan1,2, Sung Wook Baik1,2,*
Computer Systems Science and Engineering, Vol.47, No.2, pp. 1751-1773, 2023, DOI:10.32604/csse.2023.038514
Abstract Photovoltaic (PV) systems are environmentally friendly, generate green energy, and receive support from policies and organizations. However, weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits. Existing PV forecasting techniques (sequential and convolutional neural networks (CNN)) are sensitive to environmental conditions, reducing energy distribution system performance. To handle these issues, this article proposes an efficient, weather-resilient convolutional-transformer-based network (CT-NET) for accurate and efficient PV power forecasting. The network consists of three main modules. First, the acquired PV generation data are forwarded to the pre-processing module for data refinement. Next, to carry out data encoding, a… More >
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