Modelling energy demand response using long short-term memory neural networks

被引：8
|
作者：
Mesa Jimenez, Jose Joaquin [1 ,2 ,3 ]
Stokes, Lee [3 ]
Moss, Chris [3 ]
Yang, Qingping [1 ]
Livina, Valerie N. [2 ]
机构：
[1] Brunel Univ London, Kingston Lane, Uxbridge UB8 3PH, Middx, England
[2] Natl Phys Lab, Hampton Rd, Teddington TW11 0LW, Middx, England
[3] Mitie, Level 12,32 London Bridge St, London SE1 9SG, England
来源：
ENERGY EFFICIENCY | 2020年 / 13卷 / 06期
基金：
英国工程与自然科学研究理事会;
关键词：
Load forecasting; Demand side response; Machine learning; Long short-term memory; Triad forecasting; Electricity demand; Neural networks;
D O I：
10.1007/s12053-020-09879-z
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
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 withRRMSE approximate to 2.2%andMAPE approximate to 1.6%and general applicability of the methodology for demand side response programme management, with reduction of energy consumption and indirect carbon emissions.
引用
收藏
页码：1263 / 1280
页数：18
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