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Energy Household Forecast with ANN
for Demand Response and Demand Side Management

Filipe Rodrigues, Carlos Cardeira, J.M.F.Calado, R. Melício

2016/5/20

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.

Published in: Renewable Energy & Power Quality Journal (RE&PQJ),Vol. 1, Nº. 14
Pages:1016-1019 Date of Publication: 2016/5/20
ISSN: 2172-038X Date of Current Version:2016/5/4
REF:559-16 Issue Date: May 2016
DOI:10.24084/repqj14.559 Publisher: EA4EPQ

Authors and affiliations

Filipe Rodrigues(1,2), Carlos Cardeira(3), J.M.F.Calado(1,3), R. Melício(3,4)
1. Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa. Departamento de Engenharia Mecânica. Portugal
2. MIT Portugal, Porto Salvo. Portugal
3. IDMEC/LAETA, Instituto Superior Técnico, Universidade de Lisboa. Portugal
4. Departamento de Física, Escola de Ciências e Tecnologia, Universidade de Évora. Portugal

Key words

Demand Side Management, Demand Response, ANN, Household, Energy, Forecast.

References

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