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Energy consumption forecasting using genetic fuzzy rule-based systems based on MOGUL learning methodology
- Source :
- 2017 IEEE Manchester PowerTech
- Publication Year :
- 2017
- Publisher :
- Zenodo, 2017.
-
Abstract
- One of the most challenging tasks for energy domain stakeholders is to have a better preview of the electricity consumption. Having a more trustable expectation of electricity consumption can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study using a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) methodology in order to have a better profile of the electricity consumption of the following hours. The proposed approach uses the electricity consumption of the past hours to forecast the consumption value for the following hours. Results from this study are compared to those of previous approaches, namely two fuzzy based systems: and several different approaches based on artificial neural networks. The comparison of the achieved results with those achieved by the previous approaches shows that this approach can calculate a more reliable value for the electricity consumption in the following hours, as it is able to achieve lower forecasting errors, and a less standard deviation of the forecasting error results.
- Subjects :
- Consumption (economics)
Mathematical optimization
Fuzzy rule
Artificial neural network
MOGUL
Computer science
business.industry
020209 energy
Fuzzy rule based methods
02 engineering and technology
Energy consumption
Electricity consumption
Forecasting
Fuzzy logic
Standard deviation
0202 electrical engineering, electronic engineering, information engineering
Electricity
Artificial intelligence
business
Cost of electricity by source
Subjects
Details
- Language :
- English
- ISBN :
- 978-1-5090-4237-1
- ISBNs :
- 9781509042371
- Database :
- OpenAIRE
- Journal :
- 2017 IEEE Manchester PowerTech
- Accession number :
- edsair.doi.dedup.....604df1f30c2adbc2555610024c69a27d