Evolving smart meter data driven model for short-term forecasting of electric loads

Harri Niska, Pekka Koponen, Antti Mutanen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

5 Citations (Scopus)

Abstract

Short-term forecasting of electric loads is an essential function required by Smart Grids. Today increasing amount of smart metering data is available enabling the development of enhanced data-driven models for short-term load forecasting. Until now, a plethora of models have been developed ranging from simple linear regression models to more advanced models such as (artificial) neural networks (NNs) and support vector machines (SVMs). Despite the relatively high accuracy obtained, the acceptance of purely data-driven models such as NN models is still remained limited due to their complexity and nontransparent nature. Therefore it is important to develop optimization schemes, which can be used to facilitate the selection of appropriate model structure resulting good forecasting accuracy with low complexity. This study presents an optimization scheme based on multi-objective genetic algorithm (GA) for designing data-driven models for short-term forecasting of electric loads. The optimization scheme is demonstrated for designing the conventional NN/MLP model using real smart metering data and weather measurements. The optimal NN model structures are identified and analyzed in terms of model complexity and forecasting accuracy.
Original languageEnglish
Title of host publicationIntelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 IEEE Tenth International Conference on
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages1-6
ISBN (Electronic)978-1-4799-8054-3, 978-1-4799-8055-0
DOIs
Publication statusPublished - 2015
MoE publication typeA4 Article in a conference publication
EventIEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing - Singapore, Singapore
Duration: 7 Apr 20159 Apr 2015
Conference number: 10

Conference

ConferenceIEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing
Abbreviated titleISSNIP
CountrySingapore
CitySingapore
Period7/04/159/04/15

Fingerprint

Smart meters
Electric loads
Neural networks
Model structures
Linear regression
Support vector machines
Genetic algorithms

Keywords

  • smart metering
  • data mining
  • load forecasting
  • genetic algorithms

Cite this

Niska, H., Koponen, P., & Mutanen, A. (2015). Evolving smart meter data driven model for short-term forecasting of electric loads. In Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 IEEE Tenth International Conference on (pp. 1-6). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/ISSNIP.2015.7106966
Niska, Harri ; Koponen, Pekka ; Mutanen, Antti. / Evolving smart meter data driven model for short-term forecasting of electric loads. Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 IEEE Tenth International Conference on. Institute of Electrical and Electronic Engineers IEEE, 2015. pp. 1-6
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abstract = "Short-term forecasting of electric loads is an essential function required by Smart Grids. Today increasing amount of smart metering data is available enabling the development of enhanced data-driven models for short-term load forecasting. Until now, a plethora of models have been developed ranging from simple linear regression models to more advanced models such as (artificial) neural networks (NNs) and support vector machines (SVMs). Despite the relatively high accuracy obtained, the acceptance of purely data-driven models such as NN models is still remained limited due to their complexity and nontransparent nature. Therefore it is important to develop optimization schemes, which can be used to facilitate the selection of appropriate model structure resulting good forecasting accuracy with low complexity. This study presents an optimization scheme based on multi-objective genetic algorithm (GA) for designing data-driven models for short-term forecasting of electric loads. The optimization scheme is demonstrated for designing the conventional NN/MLP model using real smart metering data and weather measurements. The optimal NN model structures are identified and analyzed in terms of model complexity and forecasting accuracy.",
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Niska, H, Koponen, P & Mutanen, A 2015, Evolving smart meter data driven model for short-term forecasting of electric loads. in Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 IEEE Tenth International Conference on. Institute of Electrical and Electronic Engineers IEEE, pp. 1-6, IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Singapore, Singapore, 7/04/15. https://doi.org/10.1109/ISSNIP.2015.7106966

Evolving smart meter data driven model for short-term forecasting of electric loads. / Niska, Harri; Koponen, Pekka; Mutanen, Antti.

Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 IEEE Tenth International Conference on. Institute of Electrical and Electronic Engineers IEEE, 2015. p. 1-6.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Niska H, Koponen P, Mutanen A. Evolving smart meter data driven model for short-term forecasting of electric loads. In Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 IEEE Tenth International Conference on. Institute of Electrical and Electronic Engineers IEEE. 2015. p. 1-6 https://doi.org/10.1109/ISSNIP.2015.7106966