Solar Power Generation Forecasting With a LASSO-Based Approach

• Published in: IEEE internet of things journal 2018-04, Vol.5 (2), p.1090-1099
• Main Authors: Ningkai Tang, Shiwen Mao, Yu Wang, Nelms, R. M.
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Data points; Forecasting; Generation forecasting; Internet of Things (IoT); least absolute shrinkage and selection operator (LASSO); machine learning; Meteorology; Neural networks; Power management; Prediction algorithms; Predictive models; renewable energy; Smart grid; Solar energy; Solar power generation; Support vector machines; Training
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2018.2812155

The smart grid (SG) has emerged as an important form of the Internet of Things. Despite the high promises of renewable energy in the SG, it brings about great challenges to the existing power grid due to its nature of intermittent and uncontrollable generation. In order to fully harvest its potential, accurate forecasting of renewable power generation is indispensable for effective power management. In this paper, we propose a least absolute shrinkage and selection operator (LASSO)-based forecasting model and algorithm for solar power generation forecasting. We compare the proposed scheme with two representative schemes with three real world datasets. We find that the LASSO-based algorithm achieves a considerably higher accuracy comparing to the existing methods, using fewer training data, and being robust to anomaly data points in the training data, and its variable selection capability also offers a convenient tradeoff between complexity and accuracy, which all make the proposed LASSO-based approach a highly competitive solution to forecasting of solar power generation.