Solar Power Generation Forecasting With a LASSO-Based Approach
| Autor: | Shiwen Mao, Yu Wang, Ningkai Tang, R.M. Nelms |
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| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
Artificial neural network Computer Networks and Communications business.industry Computer science 020209 energy 02 engineering and technology Computer Science Applications Renewable energy Smart grid Lasso (statistics) Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Power grid business Solar power Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 5:1090-1099 |
| ISSN: | 2327-4662 |
| DOI: | 10.1109/jiot.2018.2812155 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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