Neural Network Frameworks for Electricity Forecasting in Mauritius and Rodrigues Islands

Ramsami, Pamela; King, Robert

doi:10.1109/powerafrica52236.2021.9543176

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Hence, the equation containing the four parameters strongly represents between F equations. It can be said that the generalization abilities of these equations are low due to the low R 2 performance of the expressions that do not include the d variable in Equations ( 8)- (11) shown in Table 4. However, when the d coefficient, which has the highest correlation value from Equations ( 1)- (7), is included in the equations, it is seen that the R 2 performances increase.…”

Section: Multi Regression Equationsmentioning

confidence: 99%

“…electricity imports and export, population, installed capacity, and gross electricity generation Ramsami and King [11] electricity demand adaptive network-based fuzzy inference system, ANN, RNN historical electricity data Bendaoud et al [12] electrical energy demand CNN load profile Sen et al [13] electricity consumption ANN-SVM population, GDP, inflation rate, and unemployment rate Tun et al [14] energy demand RNN past energy usage data Kolokas et al [15] energy demand and generation multi-step time series forecasting past energy data and weather forecasts Al-Musaylh et al [16] electricity demand online sequential extreme learning machine (OS-ELM) climate variables Moustris et al [17] load demand ANN meteorological data, cooling power index (CP)…”

Section: Introductionmentioning

confidence: 99%

“…Ramsami and King [11] have used three approaches-namely the data handling group method, ANN (feed-forward and recurrent), and adaptive network-based fuzzy inference system-to predict Mauritius and Rodrigues Islands' monthly peak electricity demand. The proposed models were utilized based on nine error metrics.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Multi Regression Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms

2023

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Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey’s mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10−14, 28.35 × 10−28, and 2.5 × 10−14, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested.

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Long-Term Electricity Load Forecasting Using Artificial Neural Network: The Case Study of Benin

Yotto

Chetangny

Zogbochi

et al. 2023

AEF

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Africans in general and specially Beninese’s low rate access to electricity requires efforts to set up new electricity production units. To satistfy the needs, it is therefore very important to have a prior knowledge of the electrical load. In this context, knowing the right need for the electrical energy to be extracted from the Beninese network in the long term and in order to better plan its stability and reliability, a forecast of this electrical load is then necessary. The study has used the annual power grid peak demand data from 2001 to 2020 to develop, train and validate the models. The electrical load peaks until 2030 are estimated as the output value. This article evaluates three algorithms of a method used in artificial neural networks (ANN) to predict electricity consumption, which is the Multilayer Perceptron (MLP) with backpropagation. To ensure stable and accurate predictions, an evaluation approach using mean square error (MSE) and correlation coefficient (R) has been used. The results have proved that the data predicted by the Bayesian regulation variant of the Multilayer Perceptron (MLP), is very close to the real data during the training and the learning of these algorithms. The validated model has developed high generalization capabilities with insignificant prediction deviations.

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Neural Network Frameworks for Electricity Forecasting in Mauritius and Rodrigues Islands

Cited by 4 publications

References 14 publications

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

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

Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms

Long-Term Electricity Load Forecasting Using Artificial Neural Network: The Case Study of Benin

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