Deep Learning-Enabled Holistic Control and Prediction System for Building Energy Consumption and Distribution Optimization

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

Electric Power Components and Systems Pub Date : 2024-05-02 DOI:10.1080/15325008.2024.2337858

Dolly Thankachamb, Senthamizh Selvi Ranganathan, Praba Devi Pachamuthu, Vasanth Ravi, Geethalakshmi Manickam, Manjunathan Alagarsamy

引用次数: 0

Abstract

Managing a building’s energy consumption while minimizing costs and grid reliance is complex. A novel system is proposed, blending advanced technologies and methodologies. Deep learning techniques,...

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基于深度学习的建筑能耗和分配优化整体控制与预测系统

管理建筑物的能源消耗，同时最大限度地降低成本和对电网的依赖是一项复杂的工作。我们提出了一种融合先进技术和方法的新型系统。深度学习技术、...

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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.