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Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine
Open AccessArticle

Prediction Interval Adjustment for Load-Forecasting using Machine Learning

1
Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada Sur No. 2501, Col. Tecnologico, Monterrey 64849, Mexico
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Instituto Nacional de Electricidad y Energías Limpias (INEEL), Av. Reforma 113, Col. Palmira, Cuernavaca CP 62490, Morelos, Mexico
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CONACYT-INEEL, Instituto Nacional de Electricidad y Energías Limpias (INEEL), Av. Reforma 113, Col. Palmira, Cuernavaca CP 62490, Morelos, Mexico
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5269; https://doi.org/10.3390/app9245269 (registering DOI)
Received: 9 November 2019 / Revised: 26 November 2019 / Accepted: 30 November 2019 / Published: 4 December 2019
(This article belongs to the Special Issue Machine Learning for Energy Forecasting)
Electricity load-forecasting is an essential tool for effective power grid operation and energy markets. However, the lack of accuracy on the estimation of the electricity demand may cause an excessive or insufficient supply which can produce instabilities in the power grid or cause load cuts. Hence, probabilistic load-forecasting methods have become more relevant since these allow an understanding of not only load-point forecasts but also the uncertainty associated with it. In this paper, we develop a probabilistic load-forecasting method based on Association Rules and Artificial Neural Networks for Short-Term Load Forecasting (2 h ahead). First, neural networks are used to estimate point-load forecasts and the variance between these and observations. Then, using the latter, a simple prediction interval is calculated. Next, association rules are employed to adjust the prediction intervals by exploiting the confidence and support of the association rules. The main idea is to increase certainty regarding predictions, thus reducing prediction interval width in accordance to the rules found. Results show that the presented methodology provides a closer prediction interval without sacrificing accuracy. Prediction interval quality and effectiveness is measured using Prediction Interval Coverage Probability (PICP) and the Dawid–Sebastiani Score (DSS). PICP and DSS per horizon shows that the Adjusted and Normal prediction intervals are similar. Also, probabilistic and point-forecast Means Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics are used. Probabilistic MAE indicates that Adjusted prediction intervals fail by less than 2.5 MW along the horizons, which is not significant if we compare it to the 1.3 MW of the Normal prediction interval failure. Also, probabilistic RMSE shows that the probabilistic error tends to be larger than MAE along the horizons, but the maximum difference between Adjusted and Normal probabilistic RMSE is less than 6 MW, which is also not significant. View Full-Text
Keywords: prediction intervals; probabilistic electricity demand forecasting; association rules; artificial neural networks; machine learning prediction intervals; probabilistic electricity demand forecasting; association rules; artificial neural networks; machine learning
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Zuniga-Garcia, M.A.; Santamaría-Bonfil, G.; Arroyo-Figueroa, G.; Batres, R. Prediction Interval Adjustment for Load-Forecasting using Machine Learning. Appl. Sci. 2019, 9, 5269.

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