A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid

Hafeez, Ghulam; Khan, Imran; Jan, Sadaqat; Shah, Ismail; Khan, Farrukh Aslam; Derhab, Abdelouahid

doi:10.1016/j.apenergy.2021.117178

Cited by 83 publications

(26 citation statements)

References 46 publications

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“…Evacuation dynamics, as well as interactions between evacuees, were not studied. Hafeez et al proposed a hybrid approach combining feature engineering and a firefly optimization algorithm with a support vector regression (SVR) model [28]. A modified firefly optimization algorithm was used to optimize the SVR's parameters.…”

Section: Related Workmentioning

confidence: 99%

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

Zong

Wang

et al. 2022

Electronics

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A two-layer artificial fish swarm evacuation model based on heterogeneous pheromones is presented in this paper. Firstly, the movements of evacuees are simulated by the behaviors of an artificial fish swarm, including preying, swarming, and following. Then, the positive feedback mechanism of heterogeneous pheromones is introduced to improve evacuation performance. Based on the interaction and communication mechanisms of biological groups of social networks in nature, the perceptual and cooperative model among individuals and between individuals and the environment is established. An optimization scheme based on fish swarms and heterogeneous pheromones is proposed. The simulation and experimental results show that the two-layer evacuation model can optimize the spatial-temporal distribution of people and can finally achieve better evacuation plans. The proposed model and algorithm can provide effective guidance for emergency safety responses and robot cooperative control in intelligent robot systems.

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Section: Related Workmentioning

confidence: 99%

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

Zong

Wang

et al. 2022

Electronics

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“…Variable selection methods are fundamental because they can identify a subset of explanatory variables that contain the most relevant information in the complete data set and, therefore, influence the accuracy of the forecast (Yang et al , 2019; Peres and Fogliatto, 2018; Abedinia et al , 2016; Shafi et al , 2019; Li et al , 2019). Aware of this importance, some authors have explored the use of attribute selection techniques mainly to accelerate training and increase accuracy of forecasting models (Hafeez et al , 2021; Kim et al , 2020; Dai and Zhao, 2020).…”

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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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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“…However, advanced computational models do better at predicting non-linear time series. Thus, scientists have switched from traditional mathematical analysis approaches to artificial-intelligence based computing technology to perform tasks more accurately using numerous techniques such as artificial neural networks (ANN) [2,18,20,27,28]and support vector machines (SVMs) [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Huang¹,

Hasan

Deng³

et al. 2022

Energy

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A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid

Cited by 83 publications

References 46 publications

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

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

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

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