Electrical energy is used throughout the globe to power devices, appliances, and strategies of transportation used in everyday life. The uses of electricity are Residential uses, industrial uses, and Transportation. This paper targeted on Forecasting Energy Consumption of a House using historical information. The proposed work uses the Radial Basis Function (RBF) Network for forecasting the demand for energy consumption of a house using historical data. The result showed that the Radial Basis Function Network performs better than FeedForward BackPropagation Network (FFBPN), and Elman BackPropagation Network (EBPN) were compared with Mean Square Error (MSE) and accuracy values.

Radial Basis Function, Neural Network, FeedForward BackPropagation Network, Elman BackPropagation Network, RBF

[1] Shiwei Yu, Ke Wang, Yi-Ming Wei “A hybrid self-adaptive Particle Swarm Optimization–Genetic Algorithm–Radial Basis Function model for annual electricity demand prediction”, Energy Conversion and Management,Volume 91, February 2015, Pages 176-185.
[2] Pedro M. Ferreira, António E. Ruano, Rui Pestana, “Improving the Identification of RBF Predictive Models to Forecast the Portuguese Electricity Consumption”, IFAC Proceedings Volumes, Volume 43, Issue 1, 2010, Pages 208-213.
[3] Mohanad S. Al-Musaylh, Ravinesh C. Deo, Jan F. Adamowski, Yan Li, “Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia”, Advanced Engineering Informatics, Volume 35, January 2018, Pages 1-16.
[4] Changhao Xia, Jian Wang, Karen McMenemy, “Short, medium and long term load forecasting model and virtual load forecasterbased on radial basis function neural networks”, International Journal of Electrical Power & Energy Systems, Volume 32, Issue 7, September 2010, Pages 743-750.
[5] Javeed Nizami, S. S. A. K., and A.Z. Al-Garni, “Forecasting electric energy consumption using neural networks,” Energy Policy, vol.23, iss. 12, pp. 1097–1104, Dec. 1995.
[6] Juan Vilar,Germán Aneiros, Paula Rana, “ Prediction intervals for Electricity demand and price using functional data”, International Journal of Electrical Power & Energy Systems, Volume 96, March 2018, Pages 457-472.
[7] Sukumar Mishra, Vivek Kumar Singh, “ Monthly Energy Consumption Forecasting Based on Windowed Momentum Neural Network”, IFAC- PapersOnline, Volume 48, Issue 2015.
[8] Pedro M. Ferreira, António E. Ruano, Rui Pestana, Laszlo T. Koczy, “Evolving RBF Predictive Models to Forecast the Portuguese Electricity Consumption”, IFAC Proceedings Volumes, Volume 42, Issue 19, 2009, Pages 414-419.
[9] Antonino Marvuglia, Antonio Messineo, “Using Recurrent Artificial Neural Networks to Forecast Household Electricity Consumption”, Energy Procedia, Volume 14,2012, Pages 45-55.
[10] Aowabin Rahman, Vivek Srikumar, Amanda D. Smith, “Predicting Electricity consumption for commercial and residential buildings using deep recurrent neural networks”, Applied Energy, Volume 212, 15 February 2018, Pages 372-385.
[11] A. S. Ahmad, M. Y. Hassan, M. P. Abdullah, H. A. Rahman, R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting”, Renewable and Sustainable Energy Reviews, Volume 33, May 2014,Pages 102-109
[12] Zuzana Majdisova, Vaclav Skala, “Radial Basis Function approximations: comparison and applications”, Applied Mathematical Modelling,Volume 51, November 2017, Pages 728-743
[13] L. G. B. Ruiz, R. Rueda, M. P. Cuéllar, M. C. Pegalajar, “Energy consumption forecasting based on Elman neural networks with evolutive optimization”, Expert Systems with Applications, Volume 92, February 2018, Pages 380-389.
[14] Michal Smolik, Vaclav Skala, Zuzana Majdisova, “Vector field radial basis function approximation”, Advances in Engineering Software,Volume 123, September 2018, Pages 117-129.
[15] Radiša Z. Jovanović, Aleksandra A. Sretenovic, Branislav D. Zivkovic, “Ensemble of various neural networks for prediction of heating energy consumption”, Energy and Buildings, Volume 94, 1 May 2015, Pages 189-199.
[16] Kunlong Chen, Jiuchun Jiang, Fangdan Zheng, Kunjin Chen, “A novel data-driven approach for residential electricity consumption prediction based on ensemble learning”, Energy, Volume 150,1 May 2018, Pages 49-60.
[17] Jihui Yuan, Craig Farnham, Chikako Azuma, Kazuo Emura, “Predictive artificial neural network models to forecast the seasonal hourly electricity consumption for a University Campus”, Sustainable Cities and Society, Volume 42, October 2018, Pages 82-92.
[18] V.O. Oladokun, A.T. Adebanjo, and O.E. Charles-Owaba, “Predicting students’ academic performance using artificial neural network:A case study of an engineering course,” Pacific Journal of Science and Technology, vol. 9, iss. 1, pp. 72-79, 2008.
[19] K. P. Amber, R. Ahmad, M. W. Aslam, A. Kousar, M. S. Khan, “Intelligent techniques for forecasting electricity consumption of buildings”, Energy, Volume 157, 15 August 2018, Pages 886-893.
[20] O.L. Usman, O.B. Alaba, “Predicting Electricity Consumption using Radial Basis Function Network”, International Journal of Computer Science and Artificial Intelligence, June 2014, Vol.4 Iss.2,PP.54-62.