Proceedings ArticleDOI
Predicting solar generation from weather forecasts using machine learning
Navin Sharma,Pranshu Sharma,David Irwin,Prashant Shenoy +3 more
- pp 528-533
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TLDR
This paper explores automatically creating site-specific prediction models for solar power generation from National Weather Service weather forecasts using machine learning techniques, and shows that SVM-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for the authors' site than existing forecast-based models.Abstract:
A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewables. One challenge with integrating renewables into the grid is that their power generation is intermittent and uncontrollable. Thus, predicting future renewable generation is important, since the grid must dispatch generators to satisfy demand as generation varies. While manually developing sophisticated prediction models may be feasible for large-scale solar farms, developing them for distributed generation at millions of homes throughout the grid is a challenging problem. To address the problem, in this paper, we explore automatically creating site-specific prediction models for solar power generation from National Weather Service (NWS) weather forecasts using machine learning techniques. We compare multiple regression techniques for generating prediction models, including linear least squares and support vector machines using multiple kernel functions. We evaluate the accuracy of each model using historical NWS forecasts and solar intensity readings from a weather station deployment for nearly a year. Our results show that SVM-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for our site than existing forecast-based models.read more
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A Model Predictive Control Approach to Microgrid Operation Optimization
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Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM
Xiangyun Qing,Yugang Niu +1 more
TL;DR: A novel solar prediction scheme for hourly day-ahead solar irradiance prediction by using the weather forecasting data is proposed and it is demonstrated that the proposed algorithm outperforms these competitive algorithms for single output prediction.
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