Electricity Demand Forecasting: The Uruguayan Case

Castrillejo, Andrés; Cugliari, Jairo; Massa, Fernando; Ramírez, Ignacio Méndez

doi:10.1007/978-3-319-99052-1_6

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Hong et al [6] during the Gefcom 2012 competition, presented a very simple linear regression model to predict the electrical charge as a function of temperature, temporal variables (hour, day, month) and some interactions.…”

Section: Literature Reviewmentioning

confidence: 99%

Linear Regression and ARIMA Models for Electricity Demand Forecasting in West Africa

Semekonawo

Kam

2022

JENRR

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This article focuses on the predictive study of electricity demand in West African countries based on the multivariate linear regression model and the ARIMA model. The objective of the study is first to establish for each country a linear regression model and ARIMA model, then to compare the two (2) models based on the MAD, RMSE and MAPE coefficients, and finally to deduce of this comparison the best valid model to establish the electricity demand prediction of the country. We have come to the conclusion that the ARIMA model is more adequate for predicting the electricity demand of most of West African countries with the exception of Gambia, Ghana, Guinea, Liberia and Nigeria where the multivariate linear regression model performs better.

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Section: Literature Reviewmentioning

confidence: 99%

Linear Regression and ARIMA Models for Electricity Demand Forecasting in West Africa

Semekonawo

Kam

2022

JENRR

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“…The practical interest of using ensemble methods has been highlighted in several works [28,39,45]. Ensemble methods are now used in very different domains: biology [5,46], medecine [24,51], electricity management [15,26,47], computer vision [27], physics [1], finance [14], ecology [6], insurance [34] or environmental sciences [13,48]. Ensemble methods are also very popular for machine learning challenges, recent software libraries based on ensemble gradient boosting methods such as XGBoost [17], CatBoost [40], LightGBM [31] are widely used in that context.…”

Section: Introductionmentioning

confidence: 99%

Boosting diversity in regression ensembles

Bourel,

Cugliari,

Goude

et al. 2023

Statistical Analysis

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Ensemble methods, such as Bagging, Boosting, or Random Forests, often enhance the prediction performance of single learners on both classification and regression tasks. In the context of regression, we propose a gradient boosting‐based algorithm incorporating a diversity term with the aim of constructing different learners that enrich the ensemble while achieving a trade‐off of some individual optimality for global enhancement. Verifying the hypotheses of Biau and Cadre's theorem (2021, Advances in contemporary statistics and econometrics—Festschrift in honour of Christine Thomas‐Agnan, Springer), we present a convergence result ensuring that the associated optimization strategy reaches the global optimum. In the experiments, we consider a variety of different base learners with increasing complexity: stumps, regression trees, Purely Random Forests, and Breiman's Random Forests. Finally, we consider simulated and benchmark datasets and a real‐world electricity demand dataset to show, by means of numerical experiments, the suitability of our procedure by examining the behavior not only of the final or the aggregated predictor but also of the whole generated sequence.

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A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

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With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision-making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in-depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short-term, medium-term, and long-term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An indepth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

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Electricity Demand Forecasting: The Uruguayan Case

Cited by 3 publications

References 23 publications

Linear Regression and ARIMA Models for Electricity Demand Forecasting in West Africa

Linear Regression and ARIMA Models for Electricity Demand Forecasting in West Africa

Boosting diversity in regression ensembles

A holistic review on energy forecasting using big data and deep learning models

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