Deploying artificial neural networks for modeling energy demand: international evidence

Bannor, B Emmanuel; Acheampong, Alex O.

doi:10.1108/ijesm-06-2019-0008

Cited by 10 publications

(6 citation statements)

References 74 publications

(105 reference statements)

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“…Besides the variables selection and contribution rate, this study also trained data with only the variables considered most important. This could have been applied in the work Bannor and Acheampong (2019), as the selection of variables can identify a subset of the most relevant information in the system, and therefore influence the energy prediction accuracy (Yang et al , 2019; Peres and Fogliatto, 2018; Abedinia et al , 2016; Shafi et al , 2019; Li et al , 2019).…”

Section: Resultsmentioning

confidence: 99%

“…In multivariate time-series data set experiments, the K number of clusters was uniformly defined for the corresponding number of classes, while the dimension was reduced by creating retained eigenvectors of the standard covariance matrix for each cluster. Bannor and Acheampong (2019) developed internationally evidenced energy demand forecasting work in Australia, China, France, India and the USA, in which eight input parameters were used to model energy demand, financial development, economic growth, industrialization, population, trade openness, Electric energy load forecasting urbanization, energy price and foreign direct investment. An MLP with backpropagation algorithm was used for training the data, and a sensitivity analysis was performed to determine the most influential variables in the system.…”

Section: Related Workmentioning

confidence: 99%

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A PCA-based variable ranking and selection approach for electric energy load forecasting

Bezerra

Grassi

Dias

et al. 2022

IJESM

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Purpose This paper aims to propose an approach based upon the principal component analysis (PCA) to define a contribution rate for each variable and then select the main variables as inputs to a neural network for energy load forecasting in the region southeastern Brazil. Design/methodology/approach The proposed approach defines a contribution rate of each variable as a weighted sum of the inner product between the variable and each principal component. So, the contribution rate is used for selecting the most important features of 27 variables and 6,815 electricity data for a multilayer perceptron network backpropagation prediction model. Several tests, starting from the most significant variable as input, and adding the next most significant variable and so on, are accomplished to predict energy load (GWh). The Kaiser–Meyer–Olkin and Bartlett sphericity tests were used to verify the overall consistency of the data for factor analysis. Findings Although energy load forecasting is an area for which databases with tens or hundreds of variables are available, the approach could select only six variables that contribute more than 85% for the model. While the contribution rates of the variables of the plants, plus energy exchange added, have only 14.14% of contribution, the variable the stored energy has a contribution rate of 26.31% being fundamental for the prediction accuracy. Originality/value Besides improving the forecasting accuracy and providing a faster predictor, the proposed PCA-based approach for calculating the contribution rate of input variables providing a better understanding of the underlying process that generated the data, which is fundamental to the Brazilian reality due to the accentuated climatic and economic variations.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A PCA-based variable ranking and selection approach for electric energy load forecasting

Bezerra

Grassi

Dias

et al. 2022

IJESM

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“…ML is a field of artificial intelligence that attempts to create and enhance computer programs that can automatically learn from past data using various algorithms. These programs can be used to study datasets, identify previously unknown trends and patterns, generalize data, develop models, or derive heuristics-based rules (Erdem Günay and Yıldırım, 2020); they are exceptionally good at detecting nonlinear correlations between input and output variables (Bannor and Acheampong, 2019). Although numerous ML algorithms are available and their numbers are continuously increasing, they mostly accomplish some specific tasks such as prediction or classification of the outcome from a new set of descriptors, clustering of the data based on the similarities of descriptors, or associating/correlating descriptors with each other and also with the output variables (Larose and Larose, 2014).…”

Section: Introductionmentioning

confidence: 99%

A critical review of machine learning for lignocellulosic ethanol production via fermentation route

2023

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In this work, machine learning (ML) applications in lignocellulosic bioethanol production were reviewed. First, the pretreatment-hydrolysis-fermentation route, the most commonly studied alternative, was summarized. Next, a bibliometric analysis was performed to identify the current trends in the field; it was found that ML applications in the field are not only increasing but also expanding their relative share in publications, with bioethanol seeming to be the most frequently researched topic while biochar and biogas are also receiving increased attention in recent years. Then, the implementation of ML for lignocellulosic bioethanol production via this route was reviewed in depth. It was observed that artificial neural network (ANN) is the most commonly used algorithm (appeared in almost 90% of articles), followed by response surface methodology (RSM) (in about 25% of articles) and random forest (RF) (in about 10% of articles). Bioethanol concentration is the most common output variable in the fermentation step, while fermentable sugar and glucose concentration are studied most in hydrolysis. The datasets are usually small, while the fitnesses of the models (R2) are usually high in the papers reviewed. Finally, a perspective for future studies, mostly considering improving data availability, was provided.

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“…Bannor and Acheampong [18] energy demand for Australia, China, France, India, and the USA ANN, MLP optimization financial development, FDI, economic growth, industrialization, population, urbanization, energy price…”

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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Deploying artificial neural networks for modeling energy demand: international evidence

Cited by 10 publications

References 74 publications

A PCA-based variable ranking and selection approach for electric energy load forecasting

A PCA-based variable ranking and selection approach for electric energy load forecasting

A critical review of machine learning for lignocellulosic ethanol production via fermentation route

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

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