A Review of AI Based Energy Consumptions Predictor
Mohammad Aamir
, Rafe Qurashi , M. Ruffea Almas , Soham Kakde
Artificial Intelligence, Machine Learning, Energy Consumption Prediction, Smart Grid, Time Series Forecasting, IoT, Renewable Energy, LSTM, Data Analytics, Energy Efficiency
Building energy use prediction plays an
important role in building energy management and
conservation as it can help us to evaluate building energy
efficiency, conduct building commissioning, and detect and
diagnose building system faults. Building energy prediction can
be broadly classified into engineering, Artificial
Intelligence (AI) based, and hybrid approaches. While
engineering and hybrid approaches use thermodynamic
equations to estimate energy use, the AI-based approach
uses historical data to predict future energy use under
constraints. Owing to the ease of use and adaptability to seek
optimal solutions in a rapid manner, the AI-based approach
has gained popularity in recent years. For this reason and to
discuss recent developments in the AI-based approaches for
building energy use prediction, this paper conducts an in-depth
review of single AI-based methods such as multiple linear
regression, artificial neural networks, and support vector
regression, and ensemble prediction method that, by
combining multiple single AI-based prediction models
improves the prediction accuracy manifold. This paper
elaborates the principles, applications, advantages and
limitations of these AI-based prediction methods and concludes
with a discussion on the future directions of the research on AI-
based methods for building energy use prediction.
"A Review of AI Based Energy Consumptions Predictor", JETNR - JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (www.JETNR.org), ISSN:2984-9276, Vol.4, Issue 4, page no.a838-a840, April-2026, Available :https://rjpn.org/JETNR/papers/JETNR2604101.pdf
Volume 4
Issue 4,
April-2026
Pages : a838-a840
Paper Reg. ID: JETNR_232928
Published Paper Id: JETNR2604101
Downloads: 00030
Research Area: Science and Technology
Country: Nagpur , Maharashtra, India
ISSN: 2984-9276 | IMPACT FACTOR: 9.87 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.87 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: RJPN (IJPublication) Janvi Wave
