A regression unsupervised incremental learning algorithm for solar irradiance prediction

• Published in: Renewable energy Vol. 164; pp. 908 - 925
• Main Authors: Puah, Boon Keat, Chong, Lee Wai, Wong, Yee Wan, Begam, K.M., Khan, Nafizah, Juman, Mohammed Ayoub, Rajkumar, Rajprasad Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2021
• Subjects: Artificial intelligence; Data decomposition; Energy management system; Incremental learning; Regression enhanced self-organizing incremental neural networks; Solar irradiance; Solar irradiance; Incremental learning; Regression enhanced self-organizing incremental neural networks; Data decomposition; Artificial intelligence; Energy management system
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2020.09.080

Intensity of solar irradiance directly affects solar power generation and this makes solar irradiance forecasting a vital process in energy management systems. Existing forecasting systems show positive solar irradiance forecasting performance, but most of them are not accurate in real-life especially when there are fast-moving clouds, causing highly fluctuating solar irradiance profile, which is difficult to predict. Moreover, the requirement to pre-train Artificial Intelligence-based forecasting system has made solar irradiance forecasting impractical as long-hour weather profiles need to be collected prior to deployment. This paper proposes a new artificial intelligent algorithm namely the Regression Enhanced Incremental Self-organising Neural Network (RE-SOINN) for accurate (even for highly fluctuating profile) and adaptive solar irradiance forecasting. This algorithm works by learning the time-series solar irradiance data incrementally and predicting it in real-time. It is novel in terms of enabling the learning of data from discrete (as in the conventional) to continuous using the regression method. The proposed algorithm further improves the prediction accuracy by decomposing the input data into two components (low and high frequency components) before feeding into the RE-SOINNs. Results showed that the proposed algorithm achieves higher accuracy compared to the Persistence model, Exponential Smoothing Model and Artificial Neural Networks.
•Intermittent solar irradiance nature complicates forecasting process.•Our AI model predicts hourly trends using timestamp and historical data only.•Data are decomposed based on trending significance for more accurate prediction.•Incremental Learning allows our model to adapt to changes in climate.•Proposed model outperforms ANN, Exponential Smoothing and Persistence Model.