THE SELECTION OF THE OPTIMAL ARCHITECTURE AND CONFIGURATION OF THE NEURAL NETWORK FOR A SHORT-TERM LOAD FORECASTING OF DEFAULT PROVIDER

<p class="Abstract">These days load forecasting is much more required in order to reduce the wastage of energy. This paper is to implement & 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 & 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 & 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 & 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>

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

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i5.11015 ◽

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 & 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 & 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 & 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 & 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>

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Improve the unit commitment scheduling by using the neural network based short term load forecasting

Conference, 2004 IEEE Industrial and Commercial Power Systems Technical ◽

10.1109/icps.2004.1314978 ◽

2004 ◽

Cited By ~ 1

Author(s):

T. Saksornchai ◽

Wei-Jen Lee ◽

K. Methaprayoon ◽

J. Liao ◽

R. Ross

Keyword(s):

Neural Network ◽

Unit Commitment ◽

Load Forecasting ◽

Short Term ◽

The Neural Network ◽

Short Term Load Forecasting

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.792.312 ◽

2015 ◽

Vol 792 ◽

pp. 312-316 ◽

Cited By ~ 1

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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