Integration of demand response and short-term forecasting for the management of prosumers' demand and generation
Autor: | Ana García-Garre, Luis Alfredo Fernández-Jiménez, Antonio Guillamón, Alberto Falces, María Del Carmen Ruiz-Abellón, Antonio Gabaldón |
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Rok vydání: | 2019 |
Předmět: |
Control and Optimization
Renewable Energy Sustainability and the Environment Computer science business.industry 020209 energy Load forecasting 020208 electrical & electronic engineering Photovoltaic system Energy Engineering and Power Technology 02 engineering and technology Energy consumption Industrial engineering Term (time) Renewable energy Demand response Electric power system short-term load forecasting demand response distributed energy resources prosumers 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Time series business Engineering (miscellaneous) Energy (miscellaneous) |
Zdroj: | RIUR. Repositorio Institucional de la Universidad de La Rioja instname Energies; Volume 13; Issue 1; Pages: 11 |
Popis: | The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure. |
Databáze: | OpenAIRE |
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