Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

• JOZI, Aria ^[2] ; PINTO, Tiago ^[1] ; PRAÇA, Isabel ^[2] ; SILVA, Francisco ^[2] ; TEIXEIRA, Brigida ^[2] ; VALE, Zita ^[3]
  1. [1] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     
  2. [2] GECAD research group - Polytechnic of Porto (ISEP/IPP)
  3. [3] Polytechnic of Porto (ISEP/IPP)

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• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 8, Nº. 1, 2019, págs. 55-64
• Idioma: inglés
• DOI: 10.14201/ADCAIJ2019815564
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• Resumen

  • 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

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