The Optimization Selection of Input Variables for Mid-Term Power Load Forecasting Based on Gradually Similar Method

2014 ◽  
Vol 610 ◽  
pp. 274-278
Author(s):  
Ji Ping Zhu
Keyword(s):  
Load Forecasting ◽  
Economic Indicators ◽  
Shanxi Province ◽  
Forecasting Accuracy ◽  
Power Load ◽  
Power Load Forecasting ◽  
Simulation Results ◽  
Input Variables ◽  
Selection Of

Gradually similar method is putted forward in the paper. The rules of selecting the independents are analyzed. And the foundations of that the variable has been permitted to enter to or eliminate from the model are described. The idea is to forecast medium and long term load of shanxi Province with using this method, and reasonable to select the economic indicators having influence on the power load. Then, these economic indicators were screened by the gradually similar method. Gradually similar method new putted forward is used for the optimization selection of the model input variables, and forecasting accuracy is discussed .Simulation results show that the method brought forward is right and feasible.

The Factors Affecting Accuracy Rate of Power Load Forecasting in Summer

2014 ◽  
Vol 1049-1050 ◽  
pp. 617-620
Author(s):  
Yan Ping Chen ◽  
Yan Li Chang
Keyword(s):  
Low Power ◽  
Load Forecasting ◽  
Paper Analysis ◽  
Accuracy Rate ◽  
Forecasting Accuracy ◽  
Factors Affecting ◽  
Power Load ◽  
Power Load Forecasting

This paper analysis the low power load forecasting accuracy in summer deep. It found the factors affecting accuracy rate of power load forecasting in summer, and proposed the measures to increase the load forecasting accuracy.

Study on Medium and Long Term Power Load Forecasting in Cold Regions

2012 ◽  
Vol 170-173 ◽  
pp. 3472-3477
Author(s):  
Jian Li ◽  
Zhen Huan Jiang
Keyword(s):  
Yellow River ◽  
Load Forecasting ◽  
Qinling Mountains ◽  
Affecting Factors ◽  
Cold Regions ◽  
Road Map ◽  
Power Load ◽  
Power Load Forecasting ◽  
Northeast Of China

A large part of Qinling Mountains and north of the Yellow River in China belongs to cold regions, so accurate forecasting of power load in cold regions plays an important role in economic development. This article summarizes the basic theory of power load, power load forecasting and medium and long term load forecasting power load forecasting in cold regions, studies on the affecting factors of the medium and long term power load in cold regions, proposes the study road map on the affecting factors of the medium and long term load in cold regions and analyzes empirically the medium and long term power load forecasting in the northeast of China.

scholarly journals Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Hla U May Marma ◽  
M. Tariq Iqbal ◽  
Christopher Thomas Seary
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Power Load ◽  
Power Load Forecasting ◽  
Long Short Term Memory

A highly efficient deep learning method for short-term power load forecasting has been developed recently. It is a challenge to improve forecasting accuracy, as power consumption data at the individual household level is erratic for variable weather conditions and random human behaviour.  In this paper, a robust short-term power load forecasting method is developed based on a Bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) neural network with stationary wavelet transform (SWT). The actual power load data is classified according to seasonal power usage behaviour. For each load classification, short-term power load forecasting is performed using the developed method. A set of lagged power load data vectors is generated from the historical power load data, and SWT decomposes the vectors into sub-components. A Bi-LSTM neural network layer extracts features from the sub-components, and an LSTM layer is used to forecast the power load from each extracted feature. A dropout layer with fixed probability is added after the Bi-LSTM and LSTM layers to bolster the forecasting accuracy. In order to evaluate the accuracy of the proposed model, it is compared against other developed short-term load forecasting models which are subjected to two seasonal load classifications.

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