Weather classification-based load and solar insolation forecasting for residential applications with LSTM neural networks

• Published in: Electrical engineering 2022-02, Vol.104 (1), p.347-361
• Main Authors: Naware, Dipanshu, Mitra, Arghya
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Data storage; Deep learning; Economic forecasting; Economics and Management; Electrical Engineering; Electrical Machines and Networks; Energy Policy; Engineering; Forecasting; Insolation; Machine learning; Mathematical models; Neural networks; Original Paper; Photovoltaic cells; Power Electronics; Renewable energy sources; Residential energy; Weather forecasting; Wind speed
• ISSN: 0948-7921; 1432-0487
• DOI: 10.1007/s00202-021-01395-2

Forecasting has always been the backbone of planning studies in the power system. With the advent of the restructured power system and also the integration of renewable energy sources, it has become more challenging. Accurate load forecasting leads to a reduction in supply–demand mismatch, whereas solar insolation/wind speed forecasting improves generation side planning. With the rapid growth in computer-based intelligence, self-learning mechanisms together with continuously growing volume of the dataset and affordable cost-effective data storage options, deep learning performance keeps getting better and has made a huge impact to solve the complex problems associated with the power system network. In this paper, we propose a classical long short-term memory neural network model for a combined day-ahead load and solar insolation forecasting of residential consumers equipped with rooftop PV based on weather classification. The effectiveness of the proposed model based on mean absolute error, mean bias error and coefficient of correlation ( R -Squared) is compared with that of the standard artificial neural network and convolutional neural network models.