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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Bibliographic Details
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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Summary: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.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0193949