Forecasting Electricity Consumption for Accurate Energy Management in Commercial Buildings With Deep Learning Models to Facilitate Demand Response Programs

IF 1.7 4区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Components and Systems Pub Date : 2024-02-19 DOI:10.1080/15325008.2024.2317353

Mustafa Yasin Erten, Nihat İnanç

引用次数: 0

Abstract

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Ener...

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利用深度学习模型预测商业楼宇用电量，促进需求响应计划的精确能源管理

在能源需求和环境问题快速增长的背景下，优化商业建筑的能源管理是一项严峻的挑战。智能电网通过先进的能源管理技术，可为商业楼宇的能源管理提供有力支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Electric Power Components and Systems 工程技术-工程：电子与电气

CiteScore

2.70

自引率

6.70%

发文量

审稿时长

7.2 months

期刊介绍： Electric Power Components and Systems publishes original theoretical and applied papers of permanent reference value related to the broad field of electric machines and drives, power electronics converters, electromechanical devices, electrical equipment, renewable and sustainable electric energy applications, and power systems. Specific topics covered include: -Electric machines- Solid-state control of electric machine drives- Power electronics converters- Electromagnetic fields in energy converters- Renewable energy generators and systems- Power system planning- Transmission and distribution- Power system protection- Dispatching and scheduling- Stability, reliability, and security- Renewable energy integration- Smart-grid and micro-grid technologies.