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Intelligent Energy Optimization in Buildings: The Synergistic Role of Smart Sensors, Building Automation Systems, and AI-Driven Analytics

Year: 2025

Type: article

Abstract: Structured Abstract:Background:As global energy consumption continues to rise, the need for optimizing energy performance in buildings has become critical. This paper explores the role of three transformative technologies—smart sensors, building automation systems (BAS), and AI-driven analytics—in enhancing energy efficiency within modern buildings. These technologies contribute significantly to reducing energy consumption, lowering operational costs, and supporting sustainable building practices.Objective:The aim of this paper is to examine the collective impact of smart sensors, BAS, and AI-driven analytics on energy optimization in buildings. By exploring practical applications, real-world case studies, and current research, we demonstrate how these systems can work together to improve energy performance.Methods:A literature review was conducted to analyze existing studies on smart sensors, BAS, and AI technologies. Real-life examples from commercial buildings, residential homes, and large office buildings were reviewed to assess the effectiveness of these technologies in reducing energy usage. The paper also explores how these technologies can be integrated to create a self-optimizing building environment.Results:The integration of smart sensors with BAS enables real-time monitoring of energy-consuming systems such as HVAC, lighting, and air quality. AI-driven analytics processes this data to optimize energy consumption through predictive modeling and automation. Case studies show that buildings implementing these technologies have achieved significant reductions in energy usage, with savings ranging from 30% to 70%. Notable examples include the Edge in Amsterdam and the Empire State Building in New York.Keywords:Smart Sensors, Building Automation Systems, AI-Driven Analytics, Energy Optimization, Sustainable Buildings, Predictive Maintenance

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15152027

Authors: Prof. Prasada Rao, Y.V. Sai Bharadwaj, Y.V. Sai Bhageerath, T.N. Gayathri, K. ShreeKalpa

Institutions: Vignana Bharathi Institute of Technology, Union Bank of India, Indian Space Research Organisation, Defence Research and Development Organisation

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Climate action

Open Access status: green

OpenAlex ID: W6911931205

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