Multi-day Load Forecasting Method In Electricity Spot Markets Based on Multiple LSTMs

2021 ◽  
Author(s):  
Guang Yang ◽  
Songhuai Du ◽  
Qingling Duan ◽  
Juan Su
Keyword(s):  
Load Forecasting ◽  
Spot Markets ◽  
Electricity Spot Markets ◽  
Forecasting Method

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.

scholarly journals Load Forecasting Based on LSTM Neural Network and Applicable to Loads of “Replacement of Coal with Electricity”

2021 ◽  
Author(s):  
Zexi Chen ◽  
Delong Zhang ◽  
Haoran Jiang ◽  
Longze Wang ◽  
Yongcong Chen ◽  
...  
Keyword(s):  
Power Systems ◽  
Short Term Memory ◽  
Load Capacity ◽  
Load Forecasting ◽  
Forecasting Method ◽  
Adequate Accuracy ◽  
Model Time Series ◽  
Power System Faults ◽  
Long Short Term Memory ◽  
Electric Loads

AbstractWith the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced firstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verification was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults.

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