Main Article Content

Aria Jozi
GECAD research group - Polytechnic of Porto (ISEP/IPP)
Tiago Pinto
Universidad de Salamanca
Isabel Praça
GECAD research group - Polytechnic of Porto (ISEP/IPP)
Francisco Silva
GECAD research group - Polytechnic of Porto (ISEP/IPP)
Brigida Teixeira
GECAD research group - Polytechnic of Porto (ISEP/IPP)
Zita Vale
Polytechnic of Porto (ISEP/IPP)
Vol. 8 No. 1 (2019), Articles, pages 55-64
Accepted: Jun 18, 2019
Copyright How to Cite


This paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel’s Fuzzy Rule Learning Method (WM). Results show that the proposed methodology achieved smaller forecasting errors for the following hours, with a smaller standard deviation. Thus, the proposed approach is able to achieve more reliable results than the other state of the art methodologies


Download data is not yet available.

Article Details


Blumsack, S., and Fernandez, A., 2012. Ready or not, here comes the smart grid!, Energy.

Conejo, Antonio, Carrión Miguel, Morales, J., 2010. Decision Making Under Uncertainty in Electricity Markets.

Cordón, O., Del Jesus, M.J., Herrera, F., and Lozano, M., 1999. MOGUL: a methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach. Int. J. Intell. Syst. 14, 1123-1153.

Dimiter Driankov, Hans Hellendoorn , Michael Reinfrank, L. Ljung, R. Palm , B. Graham, A.O., 1996. An Introduction to Fuzzy Control.

Gomes, L., Faria, P., Morais, H., Vale, Z., and Ramos, C., 2014. Distributed, agent-based intelligent system for demand response program simulation in smart grids. IEEE Intell. Syst. 29, 56-65.

Gonzalez, A., and Perez, R., 1998. Completeness and consistency conditions for learning fuzzy rules. Fuzzy Sets Syst. 96, 37-51.

Herrera, F., Lozano, M., and Verdegay, J.L., 1998. A Learning Process for Fuzzy Control Rules using Genetic Algorithms. Informatica.

Jozi, A., Pinto, T., Praça, I., Ramos, S., Vale, Z., Goujon, B., and Petrisor, T., 2017a. Energy Consumption Forecasting using Neuro-Fuzzy Inference Systems : Thales TRT building case study. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Jozi, A., Pinto, T., Praca, I., Silva, F., Teixeira, B., and Vale, Z., 2016a. Energy consumption forecasting based on Hybrid Neural Fuzzy Inference System. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI). pp. 1-5.

Jozi, A., Pinto, T., Praça, I., Silva, F., Teixeira, B., and Vale, Z., 2016b. Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature. In: Giis 2016.

Jozi, A., Pinto, T., Praça, I., Silva, F., Teixeira, B., and Vale, Z., 2017b. Energy Consumption Forecasting using Genetic fuzzy rule-based systems based on MOGUL Learning Methodology. PowerTech 2017 703689.

Kaur, N., and Kaur, A., 2016. Predictive modelling approach to data mining for forecasting electricity consumption. Proc. 2016 6th Int. Conf. - Cloud Syst. Big Data Eng. Conflu. 2016.

Marwala, L., and Twala, B., 2014. Forecasting electricity consumption in South Africa: ARMA, neural networks and neuro-fuzzy systems. 2014 Int. Jt. Conf. Neural Networks 3049-3055.

Ozoh, P., Abd-Rahman, S., Labadin, J., and Apperley, M., 2014. A Comparative Analysis of Techniques for Forecasting Electricity Consumption. Int. J. Comput. Appl. 88, 8-12.

Riza, L.S., Bergmeir, C., Herrera, F., and Benítez, J.M., 2015. {frbs}: Fuzzy Rule-Based Systems for Classification and Regression in {R}. R Packag. version 3.1-0.

Samarawickrama, N.G.I.S., Hemapala, K.T.M.U., and Jayasekara, A.G.B.P., 2016. Support Vector Machine Regression for Forecasting Electricity Demand for Large Commercial Buildings by using Kernel Parameter and Storage Effect. 2Nd Int. Mercon 2016 Moratuwa Eng. Res. Conf. 162-167.

Vinagre, E., Gomes, L., and Vale, Z., 2015. Electrical Energy Consumption Forecast Using External Facility Data. In: 2015 IEEE Symposium Series on Computational Intelligence. IEEE, pp. 659-664.