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

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



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.


[1] R.Pereira, R. Melicio, V.M.F. Mendes, J. Figueiredo, A. Fagundes, and J.C. Quadrado, “Fuzzy clustering applied to a demand response model in a smart grid contingency scenario”, in: International Power Electronics, Electrical Drives, Automation and Motion, 495-499, Ischia, Italy, 2014.
[2] F. Rodrigues, C. Cardeira, and J.M.F. Calado, “The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal”, Energy Procedia, vol. 62, pp. 220–229, 2014.
[3] I.H. Yang, and K.W. Kim, “Prediction of the time of room air temperature descending for heating systems in buildings”, Building and Environment, vol. 39, pp. 19–29, 2004.
[4] Project Eureco, “Demand side management. End-use metering campaign in 400 households of the European Community”, in: SAVE Programme, Commission of the European Communities, 2002.
[5] O. Carpinteiro, A. Reis, and A. Silva, “A hierarchical neural model in short-term load forecasting”, Applied Soft Computing, vol. 4, pp. 405-412, 2004.
[6] G. Gross, and F. Galiana, “Short-term load forecasting”, Proceedings of IEEE, vol. 75, pp. 1558-1573, 1987.
[7] J. Taylor, “Short-term load forecasting with exponentially weighted methods”, IEEE Transactions on Power Systems, vol. 27, pp. 458-464, 2012.
[8] E. Feinberg, and D. Genethliou, “Load forecasting. Applied mathematics for restructured electric power systems”, in: Optimization, Control, and Computational Intelligence, Springer, pp. 269-285, 2005.
[9] L. Saini, and M. Soni, “Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods”, in: IEEE Generation, Transmission and Distribution, vol. 149, 578-584, 2002.
[10] H. Hippert, C. Pedreira, and R. Souza, “Neural networks for short-term load forecasting: a review and evaluation”, IEEE Transactions on Power Systems, vol. 16, pp. 44-55, 2001.
[11] W. Holderbaum, R. Canart, and P. Borne, “Artificial neural networks application to boolean input systems control”, Studies in Informatics and Control, vol. 8, pp. 107-120, 1999.
[12] R.S. Zebulum, M. Vellasco, K. Guedes, and M.A. Pacheco, “Short-term load forecasting using neural nets”, in: Natural to Artificial Neural Computation, pp. 1001-1008, 1995. 1019