A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting
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 ; This work has been developed under the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO). ; info:eu-repo/semantics/publishedVersion
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting
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 ; This work has been developed under the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO). ; info:eu-repo/semantics/publishedVersion
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting
Jozi, Aria (author) / Pinto, Tiago (author) / Praça, Isabel (author) / Silva, Francisco (author) / Teixeira, Brígida (author) / Vale, Zita (author)
2019-01-01
doi:10.14201/ADCAIJ2019815564
Article (Journal)
Electronic Resource
English
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting
| BASE | 2020