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Title: Energy Household Forecast with ANN for Demand Response and Demand Side Management
Authors: Filipe, Rodrigues
Carlos, Cardeira
João, Calado
Rui, Melício
Keywords: Demand Side Management
Demand Response
Issue Date: May-2016
Abstract: This paper presents a short term load forecasting with artificial neural networks. Despite the great imprevisibility, it is possible to forecast the electricity consumption of a household with some accuracy, similarly to that the electricity utilities can do to an agglomerate of households. Nowadays, in an existing electric grid, it is important to understand and forecast household daily or hourly consumption with a reliable model for electric energy consumption and load profile. Demand response programs required this information to adequate the profile of energy load diagram to generation. In the short term load forecasting model, artificial neural networks were used, with a consumption records database. The results show that the artificial neural networks approach provides a reliable model for forecasting household electric energy consumption and load profile. To do so and using smart devices such as cyber-physical systems monitoring, gathering and computing in real time a database with weekdays and weekend, can improve forecasts results for the next hours, a strong tool for Demand Response and Demand Side Management.
Type: lecture
Appears in Collections:FIS - Comunicações - Em Congressos Científicos Internacionais

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