Forecasting household electricity demand in India: A long short-term memory approach

With the increasing demand for energy worldwide, it has become imperative to explore and adopt sustainable energy practices. In this regard, predicting household electricity consumption accurately is essential, as it can help optimize energy usage and reduce waste. This paper focuses on using Long S...

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Main Authors: Pallaiyah, Solainayagi, Kumar, Ankit, Raj, Adity, Kumar, Rohan, Nihal, Muhammed, Rajendran, Mohanapriya, Govindaraj, Jijina
Format: Conference Proceeding
Language:English
Subjects:
Electric power demand
Electricity consumption
Energy consumption
Neural networks
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creator Pallaiyah, Solainayagi
Kumar, Ankit
Raj, Adity
Kumar, Rohan
Nihal, Muhammed
Rajendran, Mohanapriya
Govindaraj, Jijina
description With the increasing demand for energy worldwide, it has become imperative to explore and adopt sustainable energy practices. In this regard, predicting household electricity consumption accurately is essential, as it can help optimize energy usage and reduce waste. This paper focuses on using Long Short-Term Memory (LSTM) shows the neural networks to prevent household electricity consumption in India. The LSTM model was implemented using the Keras library in Python. The model was trained using the training set and tested using the testing set. The study demonstrated that LSTM neural networks are a promising approach to predicting household electricity consumption in India. Further research can be conducted to recover the accuracy of the perfect by including more variables such as household size, income, and appliance usage.
doi_str_mv 10.1063/5.0193949
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source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects Electric power demand
Electricity consumption
Energy consumption
Neural networks
title Forecasting household electricity demand in India: A long short-term memory approach
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