@inproceedings{99f08fe793604512a1b28dd45c46cd65,
title = "Solar energy forecasting and optimization system for efficient renewable energy integration",
abstract = "Solar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.",
keywords = "Optimization, PV energy forecast, Renewable energy integration, Solar energy",
author = "Diana Manjarres and Ricardo Alonso and Sergio Gil-Lopez and Itziar Landa-Torres",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017 ; Conference date: 22-09-2017 Through 22-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71643-5_1",
language = "English",
isbn = "9783319716428",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1--12",
editor = "Oliver Kramer and Stuart Madnick and Woon, {Wei Lee} and Zeyar Aung",
booktitle = "Data Analytics for Renewable Energy Integration",
address = "Germany",
}