Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study

Marwen Elkamel, Lily Schleider, Eduardo L. Pasiliao, Ali Diabat and Qipeng P. Zheng
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Marwen Elkamel: Industrial Engineering & Management Systems Department, University of Central Florida, Orlando, FL 32816, USA
Lily Schleider: Industrial Engineering & Management Systems Department, University of Central Florida, Orlando, FL 32816, USA
Eduardo L. Pasiliao: Air Force Research Laboratory Munitions Directorate, Eglin Air Force Base, FL 32542, USA
Ali Diabat: Division of Engineering, New York University, Abu Dhabi 129188, UAE
Qipeng P. Zheng: Industrial Engineering & Management Systems Department, University of Central Florida, Orlando, FL 32816, USA

Energies, 2020, vol. 13, issue 15, 1-21

Abstract: Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate of model accuracy. Historical data from January 2010 to December 2017 were used. The results for the multiple regression model concluded that the variables month, Cooling Degree Days, Heating Degree Days and GDP were significant in predicting future electricity demand. Other multiple regression models were formulated that utilized other variables that were correlated to the variables in the best-selected model. These variables included: number of visitors to the state, population, number of consumers and number of households. For the CNNs, the univariant predictions had more diverse and higher Root Mean Squared Error (RMSE) values compared to the multichannel and multihead network. The multichannel network performed the best out of the three CNNs. In summary, the multichannel model was found to be the best at predicting future electricity demand out of all the models considered, including the regression model based on the datasets employed.

Keywords: electricity demand; long-term forecasting; Artificial Neural Networks; machine learning; data analytics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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