Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning-A Review

被引：4

作者：

Moghimi, Seyed Morteza ^{[1
]}

Gulliver, Thomas Aaron ^{[1
]}

Thirumai Chelvan, Ilamparithi ^{[1
]}

机构：

[1] Univ Victoria, Dept Elect & Comp Engn, STN CSC, POB 1700, Victoria, BC V8W 2Y2, Canada

来源：

ENERGIES | 2024年 / 17卷 / 03期

基金：

英国科研创新办公室;

关键词：

demand response; energy flexibility; green buildings; machine learning; optimization; smart buildings; SMART BUILDINGS; DATA-DRIVEN; LOAD; CONSUMPTION; SYSTEM; CLASSIFICATION; REGRESSION; NETWORK;

D O I：

10.3390/en17030555

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP.

引用

页数：20

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