scholarly journals Short-term forecasting of soil temperature using artificial neural network

2014 ◽  
Vol 22 (3) ◽  
pp. 576-585 ◽  
Author(s):  
Hossein Tabari ◽  
P. Hosseinzadeh Talaee ◽  
Patrick Willems
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Short Term ◽  
Artificial Neural ◽  
Short Term Forecasting

scholarly journals A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

Energies ◽  
2015 ◽  
Vol 8 (2) ◽  
pp. 1138-1153 ◽  
Author(s):  
Alberto Dolara ◽  
Francesco Grimaccia ◽  
Sonia Leva ◽  
Marco Mussetta ◽  
Emanuele Ogliari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Output ◽  
Short Term ◽  
Artificial Neural ◽  
Short Term Forecasting ◽  
Plant Power ◽  
Hybrid Artificial Neural Network

Artificial Neural Network Modelling for Short Term Load Forecasting

2021 ◽  
Author(s):  
Mahshooq Abdul Majeed ◽  
Soumya Mudgal ◽  
Lalit Tak ◽  
Janavi Popat ◽  
Harsh Kakadiya ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Short Term Load Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Modelling

An Artificial Neural Network in Short-Term Electrical Load Forecasting of a University Campus: A Case Study

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
David Palchak ◽  
Siddharth Suryanarayanan ◽  
Daniel Zimmerle
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Time Of Day ◽  
University Campus ◽  
Electrical Load ◽  
Ann Model ◽  
Short Term ◽  
Management Options ◽  
Artificial Neural

This paper presents an artificial neural network (ANN) for forecasting the short-term electrical load of a university campus using real historical data from Colorado State University. A spatio-temporal ANN model with multiple weather variables as well as time identifiers, such as day of week and time of day, are used as inputs to the network presented. The choice of the number of hidden neurons in the network is made using statistical information and taking into account the point of diminishing returns. The performance of this ANN is quantified using three error metrics: the mean average percent error; the error in the ability to predict the occurrence of the daily peak hour; and the difference in electrical energy consumption between the predicted and the actual values in a 24-h period. These error measures provide a good indication of the constraints and applicability of these predictions. In the presence of some enabling technologies such as energy storage, rescheduling of noncritical loads, and availability of time of use (ToU) pricing, the possible demand-side management options that could stem from an accurate prediction of energy consumption of a campus include the identification of anomalous events as well the management of usage.

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