Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids

Khan, Sajawal ur Rehman; Hayder, Israa Adil; Habib, Muhammad Asif; Ahmad, Mudassar; Mohsin, Syed Muhammad; Khan, Farrukh Aslam; Mustafa, Kainat

doi:10.3390/en16010276

Cited by 16 publications

(7 citation statements)

References 47 publications

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“…Modifications architectural are made by adding other feature selection methods at the RFE stage and calculating the latest ranking. The list of feature selection methods used in RFE modification includes RF-RFE [18], [19], [22]; XGB-RFE [19]; SVM-RFE [18], [20], [21], [23]; GBM-RFE [18]; Absolute Cosine [23]; KPCA [21]; dan MI [20]. To combine or figure out ranking criteria between them, you can add weights with a threshold [19]; add weights simply [18], [22]; add weights based on multiplying weights and accuracy [18]; sum with the mRMR method [23]; take the weighted average [21]; and recalculation of the smallest features and input features using the MICBC approach [20].…”

Section: Rq 5: Modification and Development Strategy Architecture Rec...mentioning

confidence: 99%

“…The list of feature selection methods used in RFE modification includes RF-RFE [18], [19], [22]; XGB-RFE [19]; SVM-RFE [18], [20], [21], [23]; GBM-RFE [18]; Absolute Cosine [23]; KPCA [21]; dan MI [20]. To combine or figure out ranking criteria between them, you can add weights with a threshold [19]; add weights simply [18], [22]; add weights based on multiplying weights and accuracy [18]; sum with the mRMR method [23]; take the weighted average [21]; and recalculation of the smallest features and input features using the MICBC approach [20]. This provides the opportunity that there are other ways to calculate feature combinations, such as merid values based on correlation, averages based on maximum weight, and so on.…”

Section: Rq 5: Modification and Development Strategy Architecture Rec...mentioning

confidence: 99%

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A Systematic Literature Review: Recursive Feature Elimination Algorithms

Priyatno,

Widiyaningtyas

2024

jitk

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Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually eliminating unimportant features. RFE has become a popular method for feature selection in various machine learning applications, such as classification and prediction. However, there is no systematic literature review (SLR) that discusses recursive feature elimination algorithms. This article conducts a SLR on RFE algorithms. The goal is to provide an overview of the current state of the RFE algorithm. This SLR uses IEEE Xplore, ScienceDirect, Springer, and Scopus (publish and publish) databases from 2018 to 2023. This SLR received 76 relevant papers with 49% standard RFEs, 43% strategy RFEs, and 8% modified RFEs. Research using RFE continues to increase every year, from 2018 to 2023. The feature selection method used simultaneously or for comparison is based on a filter approach, namely Pearson correlation, and an embedded approach, namely random forest. The most widely used machine learning algorithms are support vector machines and random forests, with 19.5% and 16.7%, respectively. Strategy RFE and modified RFE can be referred to as hybrid RFEs. Based on relevant papers, it is found that the RFE strategy is broadly divided into two categories: using RFE after other feature selection methods and using RFE simultaneously with other methods. Modification of the RFE is done by modifying the flow of the RFE. The modification process is divided into two categories: before the process of calculating the smallest weight criteria and after calculating the smallest weight criteria. Calculating the smallest weight criteria in this RFE modification is still a challenge at this time to obtain optimal results.

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Section: Rq 5: Modification and Development Strategy Architecture Rec...mentioning

confidence: 99%

Section: Rq 5: Modification and Development Strategy Architecture Rec...mentioning

confidence: 99%

A Systematic Literature Review: Recursive Feature Elimination Algorithms

Priyatno,

Widiyaningtyas

2024

jitk

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“…Khan et al [15] presented enhanced machine learning models that were based on SVM and CNN to support electricity forecasting in smart grids, using a big data set from ISO-NE. A feature selection and extraction scheme were implemented to reduce the computational complexity.…”

Section: Literature Review and Research Gap Analysismentioning

confidence: 99%

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

et al. 2023

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The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%.

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“…Haydar et al [ 6 ] proposed a framework for energy load anticipation that involved selection of attribute, extraction, as well as regression. XGB (Extreme Gradient Boosting) and RF (Random Forest) were used in the attribute selection phase to ascertain the value of each attribute.…”

Section: Introductionmentioning

confidence: 99%

A new Frontier in electric load forecasting: The LSV/MOPA model optimized by modified orca predation algorithm

Yan,

Wang,

Thwin

2024

Heliyon

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Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids

Cited by 16 publications

References 47 publications

A Systematic Literature Review: Recursive Feature Elimination Algorithms

A Systematic Literature Review: Recursive Feature Elimination Algorithms

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

A new Frontier in electric load forecasting: The LSV/MOPA model optimized by modified orca predation algorithm

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