Assessment of Input Variable Importance and Machine Learning Model Selection for Improving Short Term Load Forecasting on Different Building Types

2017 ◽  
Vol 11 (6) ◽  
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Load Forecasting ◽  
Learning Model ◽  
Variable Importance ◽  
Short Term ◽  
Machine Learning Model ◽  
Short Term Load Forecasting ◽  
Selection For

scholarly journals Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services

2020 ◽  
pp. 1-1
Author(s):  
Basheer Qolomany ◽  
Ihab Mohammed ◽  
Ala Al-Fuqaha ◽  
Mohsen Guizani ◽  
Junaid Qadir
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Smart City ◽  
Learning Model ◽  
Machine Learning Model ◽  
Industrial Iot ◽  
Selection For

scholarly journals Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Milos Kotlar ◽  
Marija Punt ◽  
Zaharije Radivojevic ◽  
Milos Cvetanovic ◽  
Veljko Milutinovic
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Anomaly Detection ◽  
Learning Model ◽  
Machine Learning Model ◽  
Automated Machine Learning

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.

Review of Machine Learning for Short Term Load Forecasting

2021 ◽  
pp. 180-190
Author(s):  
Aijia Ding ◽  
Huifen Chen ◽  
Tingzhang Liu
Keyword(s):  
Machine Learning ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

Machine learning based very short term load forecasting of machine tools

2020 ◽  
Vol 276 ◽  
pp. 115440 ◽  
Author(s):  
Bastian Dietrich ◽  
Jessica Walther ◽  
Matthias Weigold ◽  
Eberhard Abele
Keyword(s):  
Machine Learning ◽  
Machine Tools ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting
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