scholarly journals Long term load forecasting accuracy in electric utility integrated resource planning

Energy Policy ◽  
2018 ◽  
Vol 119 ◽  
pp. 410-422 ◽  
Author(s):  
Juan Pablo Carvallo ◽  
Peter H. Larsen ◽  
Alan H. Sanstad ◽  
Charles A. Goldman
Keyword(s):  
Load Forecasting ◽  
Resource Planning ◽  
Electric Utility ◽  
Forecasting Accuracy ◽  
Integrated Resource Planning

scholarly journals Load Forecasting in Electric Utility Integrated Resource Planning

2017 ◽  
Author(s):  
Juan Pablo Carvallo ◽  
Peter H. Larsen ◽  
Alan H Sanstad ◽  
Charles A. Goldman
Keyword(s):  
Load Forecasting ◽  
Resource Planning ◽  
Electric Utility ◽  
Integrated Resource Planning

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.

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.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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