One-Year-Ahead Neural Network-Based HVAC Electricity Consumption Optimization: The Influence of Occupancy Schedules

Document Type

Conference Proceeding

Source of Publication

Lecture Notes in Networks and Systems

Publication Date

1-1-2024

Abstract

Although knowing the occupancy schedule of a building can save significant energy, ensuring the heating, ventilation, and air-conditioning (HVAC) system does not run needlessly, its uncertain nature has long challenged the development of accurate long-term non-Boolean occupancy-based HVAC management systems. In this paper, we propose an occupancy-based one-year-ahead HVAC electricity consumption optimization approach using feedforward neural networks. The results confirm that including the number of occupants improves the prediction accuracy and provides an optimized profile that allows for a 33.56% of annual electricity saving, a 3.8% more than in the case where neither occupancy-based prediction nor optimization is performed.

ISBN

9789819983230

ISSN

2367-3370

Publisher

Springer Nature Singapore

Volume

839

First Page

375

Last Page

388

Disciplines

Computer Sciences

Keywords

Electricity consumption prediction and optimization, Heating, ventilation, and air-conditioning, Machine learning based electricity consumption prediction, Occupants’count schedule estimation

Scopus ID

85189502365

Indexed in Scopus

yes

Open Access

no

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