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Neural Network Based Approach for Short-Term Load Forecasting. Zainab H. Osman, Mohamed L. Awad , Tawfik K. Mahmoud Power Systems Conference and Exposition, 2009, IEEE/PES. Outline. Intro Load analysis Artificial Neural Network(ANN) structure Input variables Training data
Zainab H. Osman,
Mohamed L. Awad,
Tawfik K. Mahmoud
Power Systems Conference and Exposition, 2009, IEEE/PES
This paper analyze the relationship between factors and electricity load
By the analytical results, they choose the most correlated factors in different seasons to feed in the artificial neural networks(ANN).
Different characteristic of the power system and it’s load pattern significantly affect the ANN model. It is important to extract load characteristic such as periodicity and trends before design.
Characteristic of first week in January, April, July, October, represent four seasons
Correlation between load demand and weather parameters: temperature, dew point, wind speed, humidity
By previous load analysis, the historical load are the most correlated parameter to the forecasted load.
Temperatureare highly related to load demand in summer and spring. Temperature and humidity seem to be the most affecting weather parameter. In winter and fall wind speed effect can be negligible.
Since we forecast the hourly load, the variables are hourly values.
Training is the process to determine the ANN’s weights and biases. The training data should cover a wide range of input patterns sufficient enough to train the network
Typically, ANNs are trained following a supervised pattern, the desired output is given for each input and the training process then adjusts the weights and biases to match the desired output
In this paper, minimum distance between forecasted input variables and desire outcome is calculate for the entire database.
Data who does not achieve the condition will be filtered, in order to eliminate odd data and sudden load change due to drastic weather changes,condition :
The load pattern is divided into seven patterns represent 7 days of a week. And each season has its own training vectors
The training information is select on similar weather condition days of the forecasted day.
For each season, former 60 days of data is used for training, and the latter 30 days is for testing.
A three-layer feed forward neural network is used
2004 Egypt Unified System load, which now use regression/trending method for short-term load forecast.
Since the neural network work well on weekdays, but according to the results, it is not enough to forecast weekends and holidays
This paper use a threshold to eliminate special events in training procedure. It can be combined with other holiday and weekend forecast method.
Analysis correlation coefficients of input variables and electricity loads is a good way to decide which factors should be put in.