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

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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
- CSEE Journal of Power and Energy Systems Vol. 6, Issue 3, Pages: 563-571(2020)
- 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
  CLC：
- Published：01 September 2020
- 稿件说明：
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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：

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.

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Related Institution

Artificial Intelligence Application Department, China Electric Power Research Institute

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