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Published Paper Details:
Paper Title
Energy Consumption Forecasting For Smart Homes Using Hybrid Machine Learning And Deep Learning Models
Authors
Sriharsh Kamre, Rishwanthrao Shankupal, Diwakar Verma
Keywords
Smart Homes, Energy Forecasting, Machine Learning, Deep Learning, LSTM, Random Forest, IoT, Energy Management.
Abstract
In smart homes, predicting energy consumption is
vital, as accurate forecasting enables effective energy utilization,
achievement of sustainability targets, and lower operating costs.
In this research, we explore and design a hybrid model forecasting
framework that combines machine learning (ML) and deep
learning (DL) methodologies to predict electricity demand on a
household level in the short and long runs. The hybrid employs
Random Forest (RF) to examine and interpret complex non-linear
interactions, and Long Short-Term Memory (LSTM) to account
for the distinctive consumption behavior temporal patterns. The
environmental data used include temperature and humidity, as
well as simulated hourly smart meter data and appliance-level
usage records.Experimental we see that our hybrid model does
better than which which is the standard in the field in terms
of performance we also present.
IJCRT's Publication Details
Unique Identification Number - IJCRT2511977
Paper ID - 297679
Page Number(s) - i261-i266
Pubished in - Volume 13 | Issue 11 | November 2025
DOI (Digital Object Identifier) -
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Sriharsh Kamre, Rishwanthrao Shankupal, Diwakar Verma, "Energy Consumption Forecasting For Smart Homes Using Hybrid Machine Learning And Deep Learning Models", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 11, pp.i261-i266, November 2025, Available at :http://www.ijcrt.org/papers/IJCRT2511977.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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