A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid

Aly, Hamed H.

doi:10.1016/j.epsr.2019.106191

Cited by 129 publications

(53 citation statements)

References 17 publications

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“…Literature [ 70 ] has developed a power combination forecasting model based on decomposition, which adopts dynamic adaptive entropy weight detection method and focuses on the stability of forecasting performance. Literature [71] based on clustering technology, combined with artificial neural network (ANN), wavelet neural network (WNN) and Kalman filter (KF), a power combination forecasting model is proposed, and the results show that the model has high performance. How the combination forecasting method will develop in the future is still worth exploring.…”

Section: ) Exploration Of Combination Forecasting Methodsmentioning

confidence: 99%

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

et al. 2020

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Section: ) Exploration Of Combination Forecasting Methodsmentioning

confidence: 99%

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

et al. 2020

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“…The model is validated by outperforming the state-of-the-art algorithm. Aly [80] built a hybrid clustering method based on wavelet neural network (WNN) and ANN schemes and showed the higher performance of the proposed model, comparing it with other clustering methods.…”

Section: Short-term Load Forecastingmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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“…However, the accuracy of the forecasting process for the distribution power system is increased by including the effect of temperature, which in turn results in slow convergence. In [23], an intelligent hybrid model based on Kalman filtering (KF), WNN, and ANN using clustering techniques is presented to forecast the day and week ahead load of the commercial sector of Egypt and Canada. However, the accuracy is achieved at the cost of increased model complexity.…”

Section: Related Literature Studymentioning

confidence: 99%

A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid

et al. 2020

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Electric energy consumption forecasting enables distribution system operators to perform efficient energy management by flexibly engaging energy consumers under the intelligent demand-response program in the smart grid (SG). With this motivation, in this paper, a fast and accurate hybrid electrical energy forecasting (FA-HELF) framework is developed. The proposed framework integrates two modules with support vector machine (SVM) based forecaster. These modules are data pre-processing and feature engineering, and modified enhanced differential evolution (mEDE) based optimizer. First, feature selection algorithms like random forests and relief-F are combined to devise a hybrid feature selection algorithm to alleviate redundancy. Secondly, for feature extraction, a radial basis Kernel-based principal component analysis algorithm is employed to eliminate the dimensionality reduction problem. Finally, to conduct accurate and fast electrical energy consumption forecasting, the mEDE based optimizer is integrated with the SVM based forecaster. The resulting FA-HELF framework is tested on publicly available independent system operator New England (ISO-NE) control area hourly load data. The results demonstrate that the FA-HELF framework is robust and shows significant improvements when compared to other benchmark frameworks in terms of accuracy and convergence speed. INDEX TERMS Electrical energy consumption forecasting, energy management, smart grid, grey correlation analysis, differential evolution, radial basis kernel-based principal component analysis, support vector machine.

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A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid

Cited by 129 publications

References 17 publications

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

Artificial Intelligence Techniques in Smart Grid: A Survey

A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid

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