Application of high-order grey forecast model in the short-term load forecasting

2015 ◽  
pp. 317-322
Keyword(s):  
Load Forecasting ◽  
Forecast Model ◽  
High Order ◽  
Short Term ◽  
Short Term Load Forecasting

Short-Term Load Forecasting Using Hybrid Neural Network

2021 ◽  
Vol 12 (1) ◽  
pp. 142-156
Author(s):  
Muhammad Nadeem ◽  
Muhammad Altaf ◽  
Ayaz Ahmad
Keyword(s):  
Neural Network ◽  
New England ◽  
Low Cost ◽  
Electrical Power ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Forecast Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Multiple Variables

One of the important factors in generating low cost electrical power is the accurate forecasting of electricity consumption called load forecasting. The major objective of the load forecasting is to trim down the error between actual load and forecasted load. Due to the nonlinear nature of load forecasting and its dependency on multiple variables, the traditional forecasting methods are normally outperformed by artificial intelligence techniques. In this research paper, a robust short term load forecasting technique for one to seven days ahead is introduced based on particle swarm optimization (PSO) and Levenberg Marquardt (LM) neural network forecast model, where the PSO and LM algorithm are used for the training process of neural network. The proposed methods are tested to predict the load of the New England Power Pool region's grid and compared with the existing techniques using mean absolute percentage errors to analyze the performance of the proposed methods. Forecast results confirm that the proposed LM and PSO-based neural network schemes outperformed the existing techniques.

Monthly Load Forecasting Based on Optimum Grey Model

2011 ◽  
Vol 230-232 ◽  
pp. 1226-1230
Author(s):  
Ting Wang ◽  
Xi Miao Jia
Keyword(s):  
Historical Data ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Difficult Problem ◽  
Short Term ◽  
Original Series ◽  
Short Term Load Forecasting ◽  
Calculation Results ◽  
Long Time

Due to the variety and the randomicity of its influencing factors, the monthly load forecasting is a difficult problem for a long time. In order to improve the forecast accuracy, the paper proposes a new load forecast model based on improved GM (1, 1).First, the GM (1, 1) is used to forecast the load data, which takes the longitude historical data as original series, the increment trend of load was forecasted and takes the crosswise historical data as original series, the fluctuation trend of load was forecasted. On this basis the optimum method is led in. An optimal integrated forecasting model is built up. The case calculation results show that the proposed method can remarkably improve the accuracy of monthly load forecasting, and decrease the error. The integrated model this paper describes for short-term load forecasting is available and accurate.

One-Hour Ahead Load Forecasting Based on Wavelet Neural Networks

2015 ◽  
Vol 785 ◽  
pp. 53-57 ◽  
Author(s):  
Narin Sovann ◽  
Perumal Nallagownden ◽  
Zuhairi Baharudin
Keyword(s):  
Neural Network ◽  
New England ◽  
Wavelet Transforms ◽  
Load Forecasting ◽  
Forecast Model ◽  
Wavelet Neural Networks ◽  
Short Term ◽  
Load Forecast ◽  
Proposed Model ◽  
Short Term Load Forecasting

This paper presents the improvement on accuracy and reliability of the load forecast model. It is well-known that characteristics of a load series is a non-stationary data, which is a constraint for the load forecast methods to achieve accurate and robustness responses. To overcome this limitation, a synergized method between wavelet transform and artificial neural network is proposed for short-term load forecasting. The modeling processes such as minimizing distorted data due to convolution operator of the wavelet transforms, model inputs and neural network design are presented. The proposed method is tested using historical load data of independent system operation New England. The results of the proposed model significantly outperform either accuracy or robustness results over neural network model.

scholarly journals Short-term load forecasting using Theta method

2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek
Keyword(s):  
Empirical Studies ◽  
Load Forecasting ◽  
Seasonal Cycles ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Short Term Load Forecasting ◽  
Data Definition ◽  
System Data ◽  
Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

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