Short-Term Load Forecasting of Microgrid Based on Grey Correlation Analysis and Neural Network Optimized by Mind Evolutionary Algorithm

Yang, Haoyan; Long, Jun; He, Peng; Zheng, Yuqi

doi:10.1109/isgt-asia.2019.8881662

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…6) Radial basis function neural network (RBFNN): RBFNN is a quicker and better approach to machine learning than other ANN approaches. Hence, it is used in approximation, time series prediction, classification, and system control [39]. The structure uses radial basis functions as activation functions.…”

Section: A Energy Forecasting Methodsmentioning

confidence: 99%

Forecast-Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

Alavi

Mehran

Vahidinasab

et al. 2021

IEEE Trans. Smart Grid

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Section: A Energy Forecasting Methodsmentioning

confidence: 99%

Forecast-Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

Alavi

Mehran

Vahidinasab

et al. 2021

IEEE Trans. Smart Grid

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“…The correlation between the sequence of influencing factors (real-time electricity prices, meteorological factors such as wind speed and temperature) at the moment to be predicted and the sequence of influencing factors in the historical sample set can be analyzed by similarity calculation to effectively filter the prediction model input and achieve refinement of the model input sequence, thus improving the accuracy of the model prediction. In the similarity calculation, the gray correlation analysis method is mostly used [23].…”

Section: B Gry Correlation Analysismentioning

confidence: 99%

A Hybrid Model for Short-Term Load Forecasting Based on Novel Input Sequence Selection and CSO Optimized Depth Belief Network

Yanan,

Jiekang,

Zhen

2023

IEEE Access

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Accurate power load forecasting is crucial to the safe and stable operation of power systems. In the context of spot market, the dynamically changing real-time market tariff gives the "commodity" property of electricity and changes the electricity consumption behavior of customers and the electricity consumption at each time, which significantly aggravates the difficulty of electricity load forecasting. To address the problems of many influencing factors, difficult input sequence selection and insufficient feature extraction capability of the prediction model, we propose a hybrid prediction model that combines novel input sequence selection and longitudinal crossover algorithm (CSO) to optimize deep belief networks. Firstly, the real-time electricity price is incorporated as an influencing factor into the prediction model input, which improves the prediction model influencing factor system in the market environment; secondly, the similarity between the historical load sequence influencing factor and the sequence of load influencing factors of the day to be predicted is considered from two perspectives of distance and trend, and the historical load sequence is reasonably selected using the comprehensive similarity, and then the prediction model input sequence is determined; finally, the deep belief Finally, the load forecasting model is constructed by using deep belief network, and the key parameters such as threshold value of the forecasting model are optimized by CSO algorithm to achieve accurate power load forecasting. The proposed hybrid forecasting model is validated by comparing and analyzing the forecasting results of different input sequences and different methods with the simulation of Singapore electricity market data. In addition, input series of different time scales are set to find the best sample set to avoid the reduction of forecasting accuracy caused by data redundancy in the sample set, and a sample set of 6 months is appropriate for load forecasting by the method in this paper.INDEX TERMS Short-term load forecasting, Real-time electricity price, input Sequence Selection, Improved grey correlation analysis, Deep belief network.

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A Review of Various Neural Network Algorithms for Operation of AC Microgrids

Nasir

Bansal

Elnady

2022

2022 Advances in Science and Engineering Technology International Conferences (ASET)

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Short-Term Load Forecasting of Microgrid Based on Grey Correlation Analysis and Neural Network Optimized by Mind Evolutionary Algorithm

Cited by 5 publications

References 7 publications

Forecast-Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

Forecast-Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

A Hybrid Model for Short-Term Load Forecasting Based on Novel Input Sequence Selection and CSO Optimized Depth Belief Network

A Review of Various Neural Network Algorithms for Operation of AC Microgrids

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