Multi-Area Load Forecasting for System with Large Geographical Area S. Fan, K. Methaprayoon, W. J. Lee Industrial and Commercial Power Systems Technical Conference, IEEE/IAS 1

Outline  Intro  Regional loads and weather analysis  Multi-area load forecasting  Area partition  Support vector regression  Results  Conclusion 2

Intro.  In a power system with large geographical area, the weather and electricity demand diversity influence the forecast accuracy largely. So it is hard to predict aggregate demand by using a single forecasting model.  To overcome the problem, straightforward idea is to forecast the load separately. But a new problem occur: how to decide optimal partition/combination of the areas.  The proposed method can find optimal area partition by loads and weather characteristics, and then use support vector regression to predict each partitions load. 3

Regional loads and weather analysis  Target power system is Midwest US, major demand is residential and has been divided into 24 areas based on member-owner cooperatives 4

Regional loads  Average and peak load in each area varies in a wide range  Cause by weather and load diversity in a large area 5

Weather variation  In general, use only temperature data to measure  All mean, max and min temperature varies in 24 areas 6

Correlation between temperature and load  Covariance of two segment (left part and right part) are and 0.73, indicates string correlation between load and temperature in summer and winter 7

Load diversity 8

Multi-area load forecasting  Prior analysis demonstrate the diversity of load and temperature, so it may difficult to predict overall electricity by a universal model for the entire area  The proposed method includes:  Area partition  Support Vector Regression 9

System architecture 10

Area partition  When forecasting the aggregate electricity demand through multi-area load forecasting system, forecasting accuracy usually varies as area partition changes.  Figure by calculation experiences 11

Area partition  The optimal combination scheme could be found by exhausted method, but the computation burden is heavy.  Since the load diversity is mainly resulting from weather diversity, it is reasonable to merge adjacent areas first sue to spatial weather similarity.  Perform prediction for 24 areas individually and add them together to calculate aggregate load forecasting accuracy, and then start from corners to merge areas one by one, see if the aggregate errors decrease. 12

Area partition  Procedure :  Perform prediction for 24 areas individually and add them together to calculate aggregate load forecasting accuracy  Start from corners to merge areas one by one, if the aggregate errors decrease, accept and change the start point to the new area.  Repeat until all the area has been assigned to a group.  Different seasons have different area partition. 13

Support vector regression 14

Support vector regression 15

SVR input selection  Load between regular day and anomalous day, which include weekend, holiday and days with anomalous events, are quite different. Hence it needs different model feeders.  The input variables are hourly load values of the last day available and the similar hours in the previous days or weeks  Inputs for regular days 16

SVR input selection  Inputs for anomalous days 17

Results  24 areas hourly load and weather data has been used, Day- ahead load forecasting is performed.  March and April 2007 data are used as forecast and validate. And hole 2006 data is used for training  Pre-process data to eliminate noise(outliers) by statistic of studentized residual via dummy regression [1] [1] J. Fox, Applied Regression Analysis, Linear Models, and Related Methods,

Results  24 areas have eventually reorganized into six groups  Aggregate forecasting results 19

Results  Regional forecasting errors  The forecast error of individual area will generally increase when more areas have been partitioned.  Aggregate forecasted error is lower than most of individual area. 20

Conclusion  This paper analyze load and weather within a large area and quantify the load diversity.  The proposed method can find optimal partition under diverse load and weather condition, and achieve more accurate aggregate load forecasting. 21