Enhancing Short-Term PV Power Forecasting Using Deep Learning Models: A Comparative Study of DNN and CNN Approaches

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : S. Selvi, M. Sai Veerraju, K.V. Sandeep, Bhagavan Konduri, D. Vetrithangam

10.14445/23488379/IJEEE-V11I8P107

How to Cite?

S. Selvi, M. Sai Veerraju, K.V. Sandeep, Bhagavan Konduri, D. Vetrithangam, "Enhancing Short-Term PV Power Forecasting Using Deep Learning Models: A Comparative Study of DNN and CNN Approaches," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 8, pp. 73-80, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I8P107

Abstract:

Forecasting solar power is essential for increasing solar power plants' competitiveness in the energy market and reducing reliance on fossil fuels for social and economic advancement. The novel method of forecasting solar Photovoltaic (PV) power output through the use of deep learning and machine learning techniques specifically, Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models is presented in this research. To anticipate solar energy generation, our technique uses pertinent weather characteristics and historical PV power data as inputs. Although several PV power forecasting models in the literature have varying degrees of accuracy, our CNN and DNN models provide a clear benefit by obtaining important features from raw local data. With this skill, precise short-term projections of solar PV power output from a few hours to several days-can be made. In addition, the integration of naive seasonal forecasting with CNN and DNN models improves the precision of short-term power generation forecasts by identifying complex dependencies and periodic patterns in the data. All things considered, our suggested method offers a viable way to raise the accuracy and efficiency of solar energy forecasting, which will help the energy industry embrace sustainable energy sources more widely.

Keywords:

Solar power forecasting, Machine learning, Deep learning, Short term PV power forecasting.

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