Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms

Román-Portabales, Antón; López‐Nores, Martín; Pazos‐Arias, José J.

doi:10.3390/s21134544

Cited by 40 publications

(18 citation statements)

References 69 publications

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“…Autoregressive Integrated Moving Average (ARIMA) [7] is one of the most widely used methods for time series forecasting. On the other hand, artificial neural networks, fuzzy systems, support vector machines, and evolutionary computation are Computational Intelligence models [8] that have flexibility and the capacity to handle complex and non-linear data.…”

Section: Related Workmentioning

confidence: 99%

Consumption Forecasting and Economic-Financial Evaluation of a Brazilian Company in the Free Market

Junior¹,

Figueiredo²,

Seruffo³

et al. 2023

Learn. Nonlin. Mod.

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The essential difference between the Free Contracting Environment (FCE) and the Regulated Contracting Environment (RCE) is the possibility of freely negotiating energy terms and prices with suppliers. Disconnected from the tariffs regulated by the government, in the FCE, consumers bear the costly difference between the contracted energy and that consumed. This cost can be reduced with accurate knowledge of the consumer profile, based on the analysis of historical data. In this article, a methodology is proposed to evaluate the migration of consumers to the FCE. In a case study, graphical statistical techniques help identify the profile of a consumer in the city of Rio de Janeiro, subgroup A4 and with green tariff modality, in the period from 2016 to 2019. Then, classical and artificial neural network-based methods are used for consumption forecasting twelve months ahead. In particular, Long and Short Term Memories (LSTM) networks performed better than Autoregressive Integrated Moving Average (ARIMA) models. At the end, it is demonstrated with economic and financial indicators, the right decision of this consumer to migrate to the FCE, prior to the analysis performed in this case study.

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Section: Related Workmentioning

confidence: 99%

Consumption Forecasting and Economic-Financial Evaluation of a Brazilian Company in the Free Market

Junior¹,

Figueiredo²,

Seruffo³

et al. 2023

Learn. Nonlin. Mod.

View full text Add to dashboard Cite

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“…Moreover, the curse of dimensionality occurs when KNN is subjected to high-dimensional electrical load data. The Support Vector Regression (SVR) model is another prominent ML model for STLF and was implemented as a better alternative in [35]. In [32], a new nu support vector machine (nu-SVR) based on the tuning of a newly introduced hyperparameter called nu was recommended for STLF, which generates less error than the ANN but the selection of reasonable parameters in SVR is still a challenging task [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

et al. 2021

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Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

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“…Here, the literature aims to classify and examine the most appropriate studies for Turkey and especially for Gokceada Island. The aim is to determine which algorithms perform better for certain and specific electricity demand problems and under what conditions, including the choice of input variables and the parameters' optimal combination [4].…”

Section: Introductionmentioning

confidence: 99%

Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island

2022

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This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R2, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data.

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Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms

Cited by 40 publications

References 69 publications

Consumption Forecasting and Economic-Financial Evaluation of a Brazilian Company in the Free Market

Consumption Forecasting and Economic-Financial Evaluation of a Brazilian Company in the Free Market

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island

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