Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition

Longmao Fan; Jianing Li; Xiao-Ping Zhang

doi:10.17775/CSEEJPES.2018.01130

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition

Regular Papers | 更新时间：2026-10-15

- Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition
- Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition
- 中国电机工程学会电力与能源系统学报(英文) 2020年6卷第3期页码：563-571
- 作者机构：
  
  Department of Electronic, Electrical and Systems Engineering, University of Birmingham,Birmingham,UK
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2018.01130
  中图分类号：
- 纸质出版：2020-09-01
- 稿件说明：
移动端阅览
Longmao Fan, Jianing Li, Xiao-Ping Zhang. Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition[J]. 中国电机工程学会电力与能源系统学报(英文), 2020,6(3):563-571.

Longmao Fan, Jianing Li, Xiao-Ping Zhang. Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition[J]. CSEE Journal of Power and Energy Systems, 2020, 6(3): 563-571.
Longmao Fan, Jianing Li, Xiao-Ping Zhang. Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition[J]. 中国电机工程学会电力与能源系统学报(英文), 2020,6(3):563-571. DOI： 10.17775/CSEEJPES.2018.01130.

Longmao Fan, Jianing Li, Xiao-Ping Zhang. Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition[J]. CSEE Journal of Power and Energy Systems, 2020, 6(3): 563-571. DOI： 10.17775/CSEEJPES.2018.01130.

摘要

Abstract

Load forecasting can enhance the reliability and efficiency of operations in a home energy management system (HEMS). The rise of big data with machine learning in recent years makes it a potential solution. This paper proposes two new energy load forecasting methods

enhancing the traditional sequence to sequence long short-term memory (S2S-LSTM) model. Method 1 integrates S2S-LSTM with human behavior patterns recognition

implemented and compared by 3 types of algorithms: density based spatial clustering of applications with noise (DBSCAN)

K-means and Pearson correlation coefficient (PCC). Among them

PCC is proven to be better than the others and suitable for a large number of residential customers. Method 2 further improves Method 1's performance with a modified multi-layer Neural Network architecture

which is constituted by fully-connected

dropout and stable improved softmax layers. It optimizes the process of supervised learning in LSTM and improves the stability and accuracy of the prediction model. The performances of both proposed methods are evaluated on a dataset of 8-week electricity consumptions from 2337 residential customers.

关键词

Keywords

references

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Short-term forecasting of individual residential load based on deep learning and K-means clustering

Stochastic Planning Considering Uncertainties of Renewable Energy Seasonal Correlation and Sub-DR for Smart Distribution Network

Segmented Forecasting of Electric Load Under Pandemic Period Based on the ESD-ABiLSTMQR Method

Enhancing Information Sensing of Load Forecasting in Cyber-Physical-Social Systems: An Approach with Large Language Model

Online Probabilistic Load Forecasts Considering Data Gaps