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http://bura.brunel.ac.uk/handle/2438/21154
Title: | Modelling Energy Demand Response Using Long-Short Term Memory Neural Networks |
Authors: | Mesa Jimenez, JJ Stokes, L Yang, Q Livina, VN |
Keywords: | load forecasting;demand side response;machine learning;long-short term memory;triad forecasting;electricity demand |
Issue Date: | 24-Jul-2020 |
Publisher: | Springer Nature |
Citation: | Mesa Jiménez, J. et al. (2020) 'Modelling energy demand response using long short-term memory neural networks', Energy Efficiency, 13 (6), pp. 1263 - 1280. doi: 10.1007/s12053-020-09879-z. |
Abstract: | We propose a method for detecting and forecasting events of high energy demand, which are managed at the national level in demand side response programmes, such as the UK Triads. The methodology consists of two stages: load forecasting with long short-term memory neural network and dynamic filtering of the potential highest electricity demand peaks by using the exponential moving average. The methodology is validated on real data of a UK building management system case study. We demonstrate successful forecasts of Triad events with RRMSE ≈ 2.2% and MAPE ≈ 1.6% and general applicability of the methodology for demand side response programme management, with reduction of energy consumption and indirect carbon emissions. |
URI: | https://bura.brunel.ac.uk/handle/2438/21154 |
DOI: | https://doi.org/10.1007/s12053-020-09879-z |
ISSN: | 1570-646X |
Other Identifiers: | ORCiD: JoséJoaquìn Mesa-Jiménez https://orcid.org/0000-0003-0822-2700 ORCiD: Qingping Yang https://orcid.org/0000-0002-2557-8752 |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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