Parallel multi-step ahead power demand forecasting through NAR neural networks

2016 ◽  
Author(s):  
Riccardo Bonetto ◽  
Michele Rossi
Keyword(s):  
Neural Networks ◽  
Demand Forecasting ◽  
Power Demand

scholarly journals Deep Learning Approach to Power Demand Forecasting in Polish Power System

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6154
Author(s):  
Tomasz Ciechulski ◽  
Stanisław Osowski
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Power System ◽  
Radial Basis Function Network ◽  
Demand Forecasting ◽  
Support Vector ◽  
Learning Approach ◽  
Power Demand ◽  
Diagnostic Features ◽  
Demand Prediction

The paper presents a new approach to predicting the 24-h electricity power demand in the Polish Power System (PPS, or Krajowy System Elektroenergetyczny—KSE) using the deep learning approach. The prediction system uses a deep multilayer autoencoder to generate diagnostic features and an ensemble of two neural networks: multilayer perceptron and radial basis function network and support vector machine in regression model, for final 24-h forecast one-week advance. The period of the data that is the subject of the experiments is 2014–2019, which has been divided into two parts: Learning data (2014–2018), and test data (2019). The numerical experiments have shown the advantage of deep learning over classical approaches of neural networks for the problem of power demand prediction.

Net Demand Forecasting by Taguchi's T Method in Small or Middle Power Demand with PVs

2017 ◽  
Vol 137 (8) ◽  
pp. 1043-1051 ◽  
Author(s):  
Yusuke Morimoto ◽  
Shintaro Negishi ◽  
Satoshi Takayama ◽  
Atsushi Ishigame
Keyword(s):  
Demand Forecasting ◽  
Power Demand ◽  
Middle Power
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