A similarity based hybrid GWO-SVM method of power system load forecasting for regional special event days in anomalous load situations in Assam, India

Barman, Mayur; Choudhury, Nalin B. Dev

doi:10.1016/j.scs.2020.102311

Cited by 62 publications

(17 citation statements)

References 40 publications

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“…erefore, the training efficiency of LSTM may be low. In [21], a SVM method based on gray wolf optimization is proposed to forecast the load demands. However, SVM method needs continuous data samples, and its classification performance may not be satisfactory when the training data size is too large.…”

Section: Forecasting Methods Based On Artificial Intelligencementioning

confidence: 99%

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Jiang

Xiong

2021

Advances in Civil Engineering

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The rapid increase of the number of electric vehicles (EVs) has posed great challenges to the safe operation of the distribution network. Therefore, this paper proposes an ordered charging scheduling method for EV in the cloud-edge collaborative environment. Firstly, the uncertainty of user load demands, charging station requirements, and renewable outputs are taken into consideration. Correspondingly, the residential distribution points, EV charging stations, and renewable plants are regarded as the edge nodes. Then, the load demands and renewable outputs are predicted by a model combined with the convolutional neural network and deep belief network (CNN-DBN). Secondly, the power supply plans for charging stations are determined at the cloud side aiming at minimizing the operating cost of the distribution network via collecting the forecasting results. Finally, the charging station formulates the personalized charging scheduling strategies according to EV’s charging plans and the charging demands in order to follow the supply plan. The simulation results show that the load peak-to-valley difference and standard deviation of the proposed algorithm are reduced by 30.13% and 16.94%, respectively, compared with the disorderly charging and discharging behavior, which has verified the effectiveness and feasibility of the proposed method.

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Section: Forecasting Methods Based On Artificial Intelligencementioning

confidence: 99%

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Jiang

Xiong

2021

Advances in Civil Engineering

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“…In [62], GWO and GA were hybridized and used to solve the SCOS problem. Hybrid GWO-SVM proposed in [63] to enhance the accuracy in (PSLF) for (RSEDs). Also, GWO has been hybridized with many other methods such as hybrid GWO-SCA for optimization problems [64], GWO-ABC to enhance the premature convergence, and hybrid DE-GWO [65].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Binary Grey Wolf With Harris Hawks Optimizer for Feature Selection

et al. 2021

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Despite Grey Wolf Optimizer's (GWO) superior performance in many areas, stagnation in local optima areas may still be a concern. Several significant GWO factors can be explored to enhance the performance of selection in classification, with two conflicting concepts to be considered in using or modeling a metaheuristic method, exploring a search field, and exploiting optimal solutions. Balancing exploration and exploitation in a good manner will improve the search algorithm's performance. To achieve a good balance, this paper proposes a binary hybrid GWO and Harris Hawks Optimization (HHO) to form a memetic approach called HBGWOHHO. The sigmoid transfer function is used to transfer the continuous search space into a binary one to meet the feature selection nature requirement. A wrapper-based k-Nearest neighbor is used to evaluate the goodness of the selected features. To validate the performance of the proposed method, 18 standard UCI benchmark datasets were used. The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. The findings revealed that the proposed method was effective in improving the performance of the BGWO algorithm. The proposed hybrid method outperforms the BGWO algorithm in terms of accuracy, selected feature size, and computational time. Similarly, compared with BPSO and BGA feature selection algorithms, the proposed HBGWOHHO surpassed them yield better accuracy, the smaller size of selected features in much lower computational time.

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“…Among them, the medium-and long-term electricity consumption forecast in the power industry is the basis for achieving low-carbon power planning and evaluation, which can help the power system achieve economic and low-carbon goals. Statistics in References [1][2][3][4] show that, for every 1% reduction in the error of electricity consumption forecast, the annual operating cost of the power system will be reduced by 10 million pounds. Thus, how to improve the accuracy of power consumption prediction has always been a popular issue for researchers.…”

Section: Introductionmentioning

confidence: 99%

“…It is in line with the current development trend of power consumption forecasting but lacks quantitative empirical analysis. Besides, the medium-and long-term power demand forecasting still has the following problems: (1) the short length of historical data series leads to insufficient data samples, (2) it is difficult to improve the prediction accuracy; (3) and the economic development and climate conditions of different regions are not the same, leading to the prediction method not having a wide range of adaptability.…”

Section: Introductionmentioning

confidence: 99%

The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis

et al. 2021

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Human activities, such as energy consumption and economic development, will significantly affect the natural environment, while changes in the natural environment will also affect the sustainability of human society. Studying the energy consumption changes of human society and forecasting medium and long-term electricity demand will help realize the sustainable development of energy in future society. However, current medium- and long-term electricity consumption forecasts have insufficient data samples and the inability to consider policy impacts. Here, we develop an Economy and Policy Incorporated Computing System (EPICS), which can use artificial intelligence technology to extract the summaries of energy policy texts automatically and calculate the importance index of energy policy. It can also process economic data of different lengths to expand samples of medium- and long-term electricity consumption forecasting effectively. A forecasting method that considers policy factors and mixed-frequency economic data is introduced to estimate future social energy and power consumption. This method has shown good forecasting ability in 27 months. The effect of EPICS can be demonstrated by predicting the medium- and long-term electricity demand.

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A similarity based hybrid GWO-SVM method of power system load forecasting for regional special event days in anomalous load situations in Assam, India

Cited by 62 publications

References 40 publications

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Hybrid Binary Grey Wolf With Harris Hawks Optimizer for Feature Selection

The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis

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