A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts. (August 2018)

• Record Type: Journal Article
• Authors: Pedro, Hugo T.C.; Lim, Edwin; Coimbra, Carlos F.M.
• Is Part Of: Renewable energy. Volume 123(2018)
• Page Start: 513
• Page End: 525
• Publication Date: 2018-08
• Subjects: Renewable generation forecast -- Real-time implementation -- Nearest neighbors forecast; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.02.043
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: This paper presents a simple forecasting database infrastructure implemented using the open-source database management system MySQL. This proposal aims at advancing the myriad of solar and wind forecast models present in the literature into a production stage. The paper gives all relevant details necessary to implement a MySQL infra-structure that collects the raw data, filters unrealistic values, classifies the data, and produces forecasts automatically and without the assistance of any other computational tools. The performance of this methodology is demonstrated by creating intra-hour power output forecasts for a 1 MW photovoltaic installation in Southern California and a 10 MW wind power plant in Central California. Several machine learning forecast models are implemented (persistence, auto-regressive and nearest neighbors) and tested. Both point forecasts and prediction intervals are generated with this methodology. Quantitative and qualitative analyses of solar and wind power forecasts were performed for an extended testing period (4 years and 6 years, respectively). Results show an acceptable and robust performance for the proposed forecasts. Highlights: We propose a MySQL infrastructure to deploy real-time wind and solar forecasts. We implement several machine learning models using this methodology. The methodology is validated for intra-hour solar and wind power generation. Point forecasting and prediction interval assessment demonstrate good performance.