Short-term Load Forecasting Using Improved Similar Days Method

Qingqing Mu,Yonggang Wu,

Xiaoqiang Pan, Liangyi Huang, Xian Li

Power and Energy Engineering Conference (APPEEC), 2010 

Outline

Introduction Forecasting methods Proposed method

Index-mapping database Evaluate similarity Prediction algorithm

Improved similar-day method Experiment result Conclusion

Introduction Load forecasting can be divided into three categories:

short-term forecasts: an hour to a week Medium-term forecasts: a week to a year long-term forecasts: longer than a year.

Short-term load forecasting can help to estimate load flows, make decisions that can prevent overloading, improve network reliability and to reduce occurrences of equipment failures and blackouts.

Energy price contract evaluation on energy market

Forecasting methods Methods on forecasting

Multiplicative autoregressive model linear model Non-linear model Kalman filtering Nonparametric regression

Most popular methods are linear regression models and decompose the load into basic and weather dependent components

Proposed method Many factors influencing the daily load of power

system, such as weather condition, temperature, day type and so on.

An index-mapping database is designed for each factor to obtain mapping value. Similarity of day characteristics is introduced to evaluate the similarity between the historical day and the forecasting day.

h similar days are selected to forecast the load.

Flow charthistorical

dayscharacterist

ics

Index-mapping database

Forecasting day

characteristics

Evaluate similarity &

select h similar days

Prediction algorithm

Index-mapping database

historical daysload

Forecasting dayload

Index-mapping database Characteristics

date type : ordinary day & holiday weather situation : rainstorms week type : Mon, Tue… temperature

Similarity of different days Similarity between historical day and forecasting

day is the cosine of the angel between two vectors in m-dimensional space :

where is the set of the mapping value of m characteristics if the ith day

m

kjk

m

kik

m

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ij

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wwr

1

2

1

2

1

Prediction algorithm Select the highest h days using similarity

computation formula

Normalize the similarity of h days

The load of forecasting day is the weighted average of h days load ( represent the load of each period of ith day):

where is the forecasting load of period t on the forecasting day, and there are 96 periods a day (T=96)

Improved similar-day method

Similarity weighting There is no obvious distinction between most similar

and less similar days. Modified formula (n is set to be 110 by experiment)

Select h similar days Only high similarity days are taken, setting up a

threshold #Similarity higher than 0.6 > h, most similar h days are kept #Similarity higher than 0.6 < h, only similarity higher than

0.6 are kept

h

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ni

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Improved similar-day method

Treatment when no similar days If no similarity of historical day is higher than

the threshold, n days before these n historical day are used for forecasting, and the n days before forecasting day are abandoned.

Usually happened when suddenly weather changed.

Experiment result

Use load and weather data of a week in June 2008 in Hainan

The historical days selected is 29 days (n=29)

Experiment result

Conclusion In this paper, increasing the weight of the most

similar days, the forecasting error decreases greatly. And we made a discussion on how to select similar days and situation without similar days.

At the same time, some adjustment on certain characteristics must be made in time according to weather variance and the change of some dominant factors.

Similar days method can also combined with other methods like gray theory for load forecasting, and the result would be better.