Estimating electricity consumption at city-level through advanced machine learning methods

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-02-09 DOI:10.1080/09540091.2024.2313852

Arpad Gellert, Lorena-Maria Olaru, Adrian Florea, Ileana-Ioana Cofaru, Ugo Fiore, Francesco Palmieri

引用次数: 0

Abstract

An effective energy management system relies on the accurate prediction of electricity consumption, facilitating energy suppliers to optimise energy distribution, reduce energy waste, and avoid ove...

查看原文本刊更多论文

通过先进的机器学习方法估算城市一级的用电量

有效的能源管理系统有赖于对电力消耗的准确预测，从而帮助能源供应商优化能源分配，减少能源浪费，并避免出现能源过剩。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.