Application of Improved Grey Theory Prediction Model in Medium-Term Load Forecasting of Distribution Network

2019 ◽  
Author(s):  
Fei Gao
Keyword(s):  
Prediction Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Grey Theory ◽  
Medium Term

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

Multi-Stage Distribution Network Space Load Forecasting Method Considering Demand Side Resources

2020 ◽  
Vol 15 (12) ◽  
pp. 1474-1481
Author(s):  
Zhidong Yang ◽  
Guangjiu Chen ◽  
Jianwu Ding ◽  
Xiaojing Kang ◽  
Meng Sheng
Keyword(s):  
Distribution Network ◽  
Load Forecasting ◽  
Processing System ◽  
Prediction Method ◽  
Operation Time ◽  
Specific Information ◽  
Demand Side ◽  
Multi Stage ◽  
Prediction Function ◽  
Forecasting Method

Under the background of the further development of electric power, this paper forecasts the spatial load of distribution network, and proposes a multi-stage spatial load forecasting method considering the demand side resources. Firstly, the load of distribution network is pretreated to improve the prediction function of the processing system, and the working efficiency of the whole system is enhanced to solve the maximum load value. Then, the different conditions of demand side resources are considered step by step to realize the fine analysis, confirm the saturation density value of load, understand the specific information of spatial load, master the predicted data status, and finally carry out the comprehensive prediction method research of spatial load to realize the prediction research of spatial load of distribution network. The experimental results show that the multi-stage spatial load forecasting method considering demand side resources has high accuracy and reliability, and its forecasting effect can improve the system forecasting performance to a certain extent, reduce unnecessary operation time, reduce energy and resource consumption, and promote the development of load forecasting research.

Effective design and development of hybrid ABC-CSO-based capacitor placement with load forecasting based on artificial neural network

2019 ◽  
Vol 39 (5) ◽  
pp. 917-930 ◽  
Author(s):  
Sarika Sharma ◽  
Smarajit Ghosh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Distribution ◽  
Power Loss ◽  
Distribution Network ◽  
Load Forecasting ◽  
Distribution Networks ◽  
Content Type ◽  
Power Loss Reduction ◽  
Artificial Neural

Purpose This paper aims to develop a capacitor position in radial distribution networks with a specific end goal to enhance the voltage profile, diminish the genuine power misfortune and accomplish temperate sparing. The issue of the capacitor situation in electric appropriation systems incorporates augmenting vitality and peak power loss by technique for capacitor establishments. Design/methodology/approach This paper proposes a novel strategy using rough thinking to pick reasonable applicant hubs in a dissemination structure for capacitor situation. Voltages and power loss reduction indices of distribution networks hubs are shown by fuzzy enrollment capacities. Findings A fuzzy expert system containing a course of action of heuristic rules is then used to ascertain the capacitor position appropriateness of each hub in the circulation structure. The sizing of capacitor is solved by using hybrid artificial bee colony–cuckoo search optimization. Practical implications Finally, a short-term load forecasting based on artificial neural network is evaluated for predicting the size of the capacitor for future loads. The proposed capacitor allocation is implemented on 69-node radial distribution network as well as 34-node radial distribution network and the results are evaluated. Originality/value Simulation results show that the proposed method has reduced the overall losses of the system compared with existing approaches.

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