Optimizing energy consumption in smart homes using GA-LSTM

Akibor Junior Chukwuka 1, *, Bakare-Bolaji Moyosoreoluwa 1 and Dibba Baboucarr 2

1 Department of Computer Science, University of Sunderland, Sunderland, United Kingdom.
2 The University of Texas Rio Grande Valley, Department of Mathematical Sciences, College of Sciences, United State of America.
 
Research Article
International Journal of Science and Research Archive, 2024, 12(01), 809–819.
Article DOI: 10.30574/ijsra.2024.12.1.0792
Publication history: 
Received on 25 March 2024; revised on 18 May 2024; accepted on 20 May 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.
 
Keywords: 
Energy; GA; LSTM; Optimizing; Consumption; Prediction
 
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