The Monthly Electricity Load Forecast Based on Composite Model

2015 ◽  
Author(s):  
Xu Qifeng ◽  
Wang Qiang ◽  
Yao Zhilin ◽  
Liu Shufen
Keyword(s):  
Composite Model ◽  
Load Forecast ◽  
Electricity Load

scholarly journals A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Shubin Zheng ◽  
Qianwen Zhong ◽  
Lele Peng ◽  
Xiaodong Chai
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Load Forecasting ◽  
Forecast Model ◽  
Time Range ◽  
Simple Method ◽  
Load Forecast ◽  
Bayesian Neural Networks ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is becoming one of the key issues to solve energy crisis problem, and time-series Bayesian Neural Network is one popular method used in load forecast models. However, it has long running time and relatively strong dependence on time and weather factors at a residential level. To solve these problems, this article presents an improved Bayesian Neural Networks (IBNN) forecast model by augmenting historical load data as inputs based on simple feedforward structure. From the load time delays correlations and impact factors analysis, containing different inputs, number of hidden neurons, historic period of data, forecasting time range, and range requirement of sample data, some advices are given on how to better choose these factors. To validate the performance of improved Bayesian Neural Networks model, several residential sample datasets of one whole year from Ausgrid have been selected to build the improved Bayesian Neural Networks model. The results compared with the time-series load forecast model show that the improved Bayesian Neural Networks model can significantly reduce calculating time by more than 30 times and even when the time or meteorological factors are missing, it can still predict the load with a high accuracy. Compared with other widely used prediction methods, the IBNN also performs a better accuracy and relatively shorter computing time. This improved Bayesian Neural Networks forecasting method can be applied in residential energy management.

scholarly journals Micro-Spatial Electricity Load Forecasting Clustering Technique

2020 ◽  
Vol 202 ◽  
pp. 11005
Author(s):  
Christine Widyastuti ◽  
Adri Senen ◽  
Oktaria Handayani
Keyword(s):  
Load Forecasting ◽  
Similar Data ◽  
Load Forecast ◽  
Load Growth ◽  
Investment Cost ◽  
Accurate Forecast ◽  
Distribution Station ◽  
Electricity Load ◽  
Micro Grids ◽  
Data Group

Low growth of electricity load forecast eliminates cost opportunity of electricity sale due to unserviceable load demands. Meanwhile, if it is exorbitant, it will cause over-investment and incriminate investment cost. Existing method of sector load is simplified and easy to implement. However, the accuracy tends to bias over one area of which data is limited and dynamic service area. Besides, the results of its forecast is macro-based, which means it is unable to show load centres in micro grids and failed to locate the distribution station. Therefore, we need micro-spatial load forecasting. By using micro-spatial load forecast, the extrapolated areas are grouped into grids. Clustering analysis is used for grouping the grids. It generates similarity matrix of similar data group. Clustering involves factors causing load growth at each grid; geography, demography, socio-economic, and electricity load per sector. Results of every cluster consist of different regional characteristics, which later the load growth is projected as to obtain more accurate forecast..

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