@misc{10481/88639,
year = {2021},
month = {3},
url = {https://hdl.handle.net/10481/88639},
abstract = {Electricity demand is shown to steadily increase in the last few years, and it is one of the key aspects of living standards and quantifying welfare effects. However, the irregularity of electricity demand is one of the main problems in this field. Therefore, it is important to accurately anticipate future expenditures in order to optimize energy generation and to avoid unexpected wastes. As a result, we developed Machine Learning models to predict electricity demand. In particular, our study has been performed using data of the Spanish Electricity Network from 2007 to 2019. To this end, we propose the implementation of a set of Machine Learning techniques using various frameworks. In particular, we implemented six different prediction models: Linear Regression, Regression Trees, Gradient Boosting Regression, Random Forests, Multi-layer Perceptron, and three types of recurrent neural networks. Our experimentation shows promising results in all cases, since our models provides better prediction than the one estimated by the Spanish Electricity Network with an improvement of 12% in the worst case and up to 37% for the best predictor, which turned out to be the Gated Recurrent Unit neural network.},
organization = {TIC111},
publisher = {Elsevier},
keywords = {Deep learning},
keywords = {Electricity demand},
keywords = {Machine learning},
keywords = {Time-series forecasting},
title = {Analysis and enhanced prediction of the Spanish Electricity Network through Big Data and Machine Learning techniques},
doi = {10.1016/j.ijar.2021.03.002},
author = {Pegalajar Jiménez, María Del Carmen and Baca Ruiz, Luis Gonzaga and Pegalajar Cuéllar, Manuel and Rueda Delgado, Ramón},
}