Adaptive solar power generation forecasting using enhanced neural network with weather modulation

Article type: Research Article

Authors: Sujeeth, T.^{a; *} | Ramesh, C.^b | Palwe, Sushila^c | Ramu, Gandikota^d | Basha, Shaik Johny^e | Upadhyay, Deepak^f | Chanthirasekaran, K.^g | Sivasankari, K.^h | Rajaram, A.ⁱ

Affiliations: [a] Department of CSE, Siddartha Educational Academy Group of Institutions, Tirupati, Andhra Pradesh, India | [b] Department of Mechanical Engineering, M. Kumarasamy College of Engineering, Karur, India | [c] Department of Computer Engineering and Technology, MIT-WPU, Maharashtra, India | [d] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India | [e] Department of CSE, Lakireddy Bali Reddy College of Engineering (A), Mylavaram, Andhra Pradesh, India | [f] Department of Computer Science and Engineering, Graphic Era hill univeristy, Dehradun, India | [g] Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai, India | [h] Department of Electronics and Communication Engineering, Akshaya College of Engineering and Technology, Coimbatore, India | [i] Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, India

Correspondence: [*] Corresponding author. Dr. T. Sujeeth, Department of CSE, Siddartha Educational Academy Group of Institutions, Tirupati, Andhra Pradesh, India. E-mail: [email protected]

Abstract: Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices.

Keywords: Solar power generation, forecasting, artificial neural network, machine learning, renewable energy, grid management

DOI: 10.3233/JIFS-235612

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10955-10968, 2024