Optimizing energy consumption in smart homes using GA-LSTM

Authors

Akibor Junior Chukwuka
Bakare-Bolaji Moyosoreoluwa
Baboucarr Dibba, The University of Texas Rio Grande Valley

Document Type

Article

Publication Date

2024

Abstract

The need to optimize energy consumption arises from the inadequate energy supply many homes face. However, to optimize energy consumption in a home, one must be equipped with the knowledge of the energy consumption rate and energy supply rate in the home. This paper proposed the use of a Long Short-Term Memory (LSTM) model optimized by Genetic Algorithm (GA) to optimize the energy consumption in a smart home. The model was designed using 8 input variables, which were observed weather information of a given region over a span of 350 days. The data set was split into a training data set and a test data set in the ratio 4:1. Both the baseline LSTM model and our proposed GA-LSTM model was used to train and test the data. The results of the baseline LSTM model showed a Mean Average Percentage Error (MAPE) of 0.15 and a Root Mean Square Error (RMSE) of 0.0007, while our proposed GA-LSTM recorded an MAPE of 0.07 and RMSE of 0.0004. The implication of these results is a better performance of our proposed GA-LSTM model over the baseline LSTM model. This paper demonstrated how the Genetic Algorithm was able to optimize the baseline LSTM model by selecting the best weights during the LSTM training process. The findings of this paper are recommended for smart homes having the capability to monitor energy usage of installed electrical appliances and the ability to predict to weather condition of the region the home is sited.

Comments

Student publication. Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

Recommended Citation

Chukwuka, Akibor Junior, Bakare-Bolaji Moyosoreoluwa, and Dibba Baboucarr. "Optimizing energy consumption in smart homes using GA-LSTM." International Journal of Science and Research Archive 12, no. 1 (2024): 809-819. https://doi.org/10.30574/ijsra.2024.12.1.0792

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

International Journal of Science and Research Archive

DOI

https://doi.org/10.30574/ijsra.2024.12.1.0792