Improve the Unit Commitment Scheduling by Using the Neural-Network-Based Short-Term Load Forecasting

2005 ◽  
Vol 41 (1) ◽  
pp. 169-179 ◽  
Author(s):  
T. Saksornchai ◽  
W.-J. Lee ◽  
K. Methaprayoon ◽  
J.R. Liao ◽  
R.J. Ross
Keyword(s):  
Neural Network ◽  
Unit Commitment ◽  
Load Forecasting ◽  
Short Term ◽  
The Neural Network ◽  
Short Term Load Forecasting

Comparative Study Of Short Term Load Forecasting Using Multilayer Feed Forward Neural Network With Back Propagation Learning And Radial Basis Functional Neural Network

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Amit Tiwari ◽  
Adarsh Dhar Dubey ◽  
And Devesh Patel
Keyword(s):  
Neural Network ◽  
Power System ◽  
Structural Changes ◽  
Unit Commitment ◽  
Network Reliability ◽  
Peak Load ◽  
Load Forecasting ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Short Term Load Forecasting

The term load forecast refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that important power system expansion decisions can be made. Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling & for system security such as peak load shaving by power interchange with interconnected utilities. With structural changes to electricity in recent years, there is an emphasis on Short Term Load Forecasting (STLF).STLF is the essential part of power system planning & operation. Basic operating functions such as unit commitment, economic dispatch, and fuel scheduling & unit maintenance can be performed efficiently with an accurate forecast. Short term load forecasting can help to estimate load flows & to make decisions that can prevent overloading. Timely implementations of such decisions lead to improvement of network reliability & to the reduced occurrences of equipment failures & blackouts. The aim of short term load forecasting is to predict future electricity demands based, traditionally on historical data and predicted weather conditions. Short term load forecasting in its basic form is a statistical problem, where in the previous load values (time series variables) and influencing factors (casual variables) are used to determine the future loads.

scholarly journals A Non-Linear Controller for Forecasting the Rising Demand for Electric Vehicles Applicable to Indian Road Conditions

2016 ◽  
Vol 6 (5) ◽  
pp. 2274
Author(s):  
Poorani S ◽  
Murugan R
Keyword(s):  
Neural Network ◽  
Historical Data ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Ann Model ◽  
Short Term ◽  
The Neural Network ◽  
Short Term Load Forecasting ◽  
Temperature And Humidity ◽  
Charging Stations

<p class="Abstract">These days load forecasting is much more required  in order to reduce the wastage of energy. This paper is to implement &amp; develop the idea of short term load forecasting by using Artificial Neural Network, the design of the neural network model, input data selection and Training &amp; Testing by using short term load forecasting will be described in paper. For the EV load forecasting only 2 variables are being used as temperature and humidity to forecast the output as load. This type of designed ANN model will be mapped by using historical data of temperature and humidity (taken from meteorological sites), whereas it is being Trained &amp; Tested by using historical data of loading of EV charging stations (Chetan maini ,Bangalore) of a particular area as Coimbatore to give the desired result. Training &amp; Testing done by using large amount of historical data of weather conditions and loading data (kV). By the help of this model they can predict their daily loads (next day's load) by putting historical data in the acquired algorithm.</p>

A Non-Linear Controller for Forecasting the Rising Demand for Electric Vehicles Applicable to Indian Road Conditions

2016 ◽  
Vol 6 (5) ◽  
pp. 2274
Author(s):  
Poorani S ◽  
Murugan R
Keyword(s):  
Neural Network ◽  
Historical Data ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Ann Model ◽  
Short Term ◽  
The Neural Network ◽  
Short Term Load Forecasting ◽  
Temperature And Humidity ◽  
Charging Stations

<p class="Abstract">These days load forecasting is much more required  in order to reduce the wastage of energy. This paper is to implement &amp; develop the idea of short term load forecasting by using Artificial Neural Network, the design of the neural network model, input data selection and Training &amp; Testing by using short term load forecasting will be described in paper. For the EV load forecasting only 2 variables are being used as temperature and humidity to forecast the output as load. This type of designed ANN model will be mapped by using historical data of temperature and humidity (taken from meteorological sites), whereas it is being Trained &amp; Tested by using historical data of loading of EV charging stations (Chetan maini ,Bangalore) of a particular area as Coimbatore to give the desired result. Training &amp; Testing done by using large amount of historical data of weather conditions and loading data (kV). By the help of this model they can predict their daily loads (next day's load) by putting historical data in the acquired algorithm.</p>

Efficiency of Using Artificial Neural Network for Short-Term Load Forecasting

2015 ◽  
Vol 792 ◽  
pp. 312-316 ◽  
Author(s):  
Svetlana Rodygina ◽  
Valentina Lyubchenko ◽  
Alexander Rodygin
Keyword(s):  
Neural Network ◽  
Electricity Market ◽  
Load Forecasting ◽  
Operating Conditions ◽  
Short Term ◽  
The Neural Network ◽  
Short Term Load Forecasting ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Short Term Forecasting

Using artificial neural networks (ANN) for short-term load forecasting is an efficient method to get the best result. Considered problem of short-term load forecasting shows that the accuracy of short-term forecasting models and methods significantly influences on the further planning of operating conditions at the modern electricity market. The obtained error for short-term load forecasting using the neural network algorithm is 2.78%.

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