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>

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>

Short Term Load Forecasting Based on Improved RBF Neural Network

2013 ◽  
Vol 860-863 ◽  
pp. 2610-2613
Author(s):  
Hong Zhang ◽  
Zhi Guo Lei ◽  
Jian Guo ◽  
Zhao Yu Pian
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Short Term Load Forecasting ◽  
Radial Basis

An improved radial basis function neural network is proposed that preprocessing is the key to improving the precision of short-term load forecasting. This paper presents a new model which is based on classical RBF neural network, combine the GA-optimized SVM radial basis function and RBF neural network. According to the date of the type, temperature, weather conditions and other factors ,The Application of combined GA-optimized SVM radial basis function is used to extract useful data to improve the load forecasting accuracy of RBF neural network. Spring load data of California were applied for simulation. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.

scholarly journals Daily Peak Load Forecast Using Artificial Neural Network

2019 ◽  
Vol 9 (4) ◽  
pp. 2256
Author(s):  
Ramesh Kumar V ◽  
Pradipkumar Dixit
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Peak Load ◽  
Load Forecasting ◽  
Percentage Error ◽  
Ann Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Artificial Neural

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.

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

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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