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 Network topology Result Conclusion

Introduction 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).

Load analysis 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.

Load analysis Characteristic of first week in January, April, July, October, represent four seasons

Load analysis

Load analysis Correlation between load demand and weather parameters: temperature, dew point, wind speed, humidity Fall spring

ANN structure – input variables By previous load analysis, the historical load are the most correlated parameter to the forecasted load. Temperature are 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.

ANN structure – training data 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

ANN structure – training data 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 : 𝑃 𝑑,𝑡 − 𝑃 ^ (𝑑,𝑡) < 𝑎 𝑖

ANN structure – training data 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.

ANN structure – network topology A three-layer feed forward neural network is used Output layer Hidden layer Input layer

ANN structure – network topology The approach of selecting proper number of hidden neurons is: Set the estimated optimal number of neurons as square root of the product of number of inputs times number of outputs. Increment by one to find the minimum forecast error

ANN structure – network topology Inputs of ANN common Load value of previous hour The load value of one, two and three days preceding the forecasted day at the same and the previous hour. The load value of the same and previous hour in previous week The forecasted hourly temperature, dew point, relative humidity and wind speed Summer and Spring : previous hour’s temperature, dew point, relative humidity and wind speed Winter and Fall : only previous hour’s temperature

Result 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

Result Include/exclude the effect of weekends and holidays Proposed method Conventional regression method

Conclusion 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.