Comparison of Short-Term Load Forecasting Performance by Neural Network and Autoregressive Based Models

2018 ◽  
Author(s):  
M. Lopez ◽  
S. Valero ◽  
C. Sans ◽  
C. Senabre ◽  
A. Gabaldon
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Performance ◽  
Short Term Load Forecasting

scholarly journals The Short-Term Load Forecasting for Special Days Based on Bagged Regression Trees in Qingdao, China

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Huanhe Dong ◽  
Ya Gao ◽  
Yong Fang ◽  
Mingshuo Liu ◽  
Yuan Kong
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Regression Trees ◽  
Short Term ◽  
Indicator Variable ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Short Term Load Forecasting ◽  
Artificial Neural Network Ann ◽  
Improved Model

There are many factors that affect short-term load forecasting performance, such as weather and holidays. However, most of the existing load forecasting models lack more detailed considerations for some special days. In this paper, the applicability of the bagged regression trees (BRT) model combined with eight variables is investigated to forecast short-term load in Qingdao. The comparative experiments show that the accuracy and speed of forecasting have some improvements using the BRT than the artificial neural network (ANN). Then, an indicator variable is newly proposed to capture the abnormal information during special days, which include national statutory holidays, bridging days, and proximity days. The BRT model combined with this indicator variable is tested on the load series measured in 2018. Experiments demonstrate that the improved model generates more accurate predictive results than BRT model combined with previously variables on special days.

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

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