DBSCAN-based energy users clustering for performance enhancement of deep learning model

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-05 DOI:10.3233/jifs-235873
Khursheed Aurangzeb
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Abstract

Background:Due to rapid progress in the fields of artificial intelligence, machine learning and deep learning, the power grids are transforming into Smart Grids (SG) which are versatile, reliable, intelligent and stable. The power consumption of the energy users is varying throughout the day as well as in different days of the week. Power consumption forecasting is of vital importance for the sustainable management and operation of SG. Methodology:In this work, the aim is to apply clustering for dividing a smart residential community into several group of similar profile energy user, which will be effective for developing and training representative deep neural network (DNN) models for power load forecasting of users in respective groups. The DNN models is composed of convolutional neural network (CNN) followed by LSTM layers for feature extraction and sequence learning respectively. The DNN For experimentation, the Smart Grid Smart City (SGSC) project database is used and its energy users are grouped into various clusters. Results:The residential community is divided into four groups of customers based on the chosen criterion where Group 1, 2, 3 and 4 contains 14 percent, 22 percent, 19 percent and 45 percent users respectively. Almost half of the population (45 percent) of the considered residential community exhibits less than 23 outliers in their electricity consumption patterns. The rest of the population is divided into three groups, where specialized deep learning models developed and trained for respective groups are able to achieve higher forecasting accuracy. The results of our proposed approach will assist researchers and utility companies by requiring fewer specialized deep-learning models for accurate forecasting of users who belong to various groups of similar-profile energy consumption.
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基于 DBSCAN 的能量用户聚类用于提升深度学习模型的性能
背景:由于人工智能、机器学习和深度学习领域的快速发展,电网正在向多功能、可靠、智能和稳定的智能电网(SG)转变。能源用户的用电量在一天中和一周中的不同日子里都会发生变化。用电量预测对于智能电网的可持续管理和运行至关重要。方法:在这项工作中,目的是应用聚类方法将智能住宅小区划分为几个具有相似特征的能源用户组,从而有效地开发和训练具有代表性的深度神经网络(DNN)模型,用于预测各组用户的电力负荷。DNN 模型由卷积神经网络(CNN)和 LSTM 层组成,分别用于特征提取和序列学习。DNN 在实验中使用了智能电网智慧城市(SGSC)项目数据库,并将其能源用户分为不同的群组。结果:根据所选标准,住宅社区被分为四组用户,其中第 1、2、3 和 4 组分别包含 14%、22%、19% 和 45% 的用户。近一半的居民(45%)在其用电模式中表现出少于 23 个异常值。其余用户被分为三组,在这三组中,针对各自组别开发和训练的专业深度学习模型能够实现更高的预测精度。我们提出的方法的结果将有助于研究人员和公用事业公司,只需较少的专业深度学习模型,就能对属于不同相似能源消耗群体的用户进行准确预测。
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来源期刊
Journal of Intelligent & Fuzzy Systems
Journal of Intelligent & Fuzzy Systems 工程技术-计算机:人工智能
CiteScore
3.40
自引率
10.00%
发文量
965
审稿时长
5.1 months
期刊介绍: The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
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