Demand forecasting at low aggregation levels using Factored Conditional Restricted Boltzmann Machine

Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Emil Mahler Larsen, Pierre Pinson

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The electrical demand forecasting problem can be regarded as a non-linear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high resolution. To solve this challenging problem, various time series and machine learning approaches has been proposed in the literature. As an evolution of neural network-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by being stochastic and allowing bi-directional connections between neurons. In this paper, we investigate a newly developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for demand forecasting. The assessment is made on the EcoGrid EU dataset, consisting of aggregated electric power consumption, price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to real-time pricing signals. The results show that for the energy prediction problem solved here, FCRBM outperforms the benchmark machine learning approach, i.e. Support Vector Machine.

Original languageEnglish
Title of host publication19th Power Systems Computation Conference, PSCC 2016
Subtitle of host publication20-24 June 2016, Genoa, Italy
Place of PublicationPiscataway, NJ
Number of pages7
ISBN (Electronic)978-88-941051-2-4
ISBN (Print)978-1-4673-8151-2
Publication statusPublished - 10 Aug 2016
Externally publishedYes
Event19th Power Systems Computation Conference, PSCC 2016 - Genova, Italy
Duration: 20 Jun 201624 Jun 2016
Conference number: 19


Conference19th Power Systems Computation Conference, PSCC 2016
Abbreviated titlePSCC
Internet address


  • Deep learning
  • Energy prediction
  • Factored conditional restricted Boltzmann machine
  • Support vector machine

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