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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
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Outline Introduction Forecasting methods Proposed method Index-mapping database Evaluate similarity Prediction algorithm Improved similar-day method Experiment result Conclusion
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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
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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
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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.
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Flow chart historical days characteristic s Index-mapping database Forecasting day characteristics Evaluate similarity & select h similar days Prediction algorithm Index-mapping database historical days load Forecasting day load
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Index-mapping database Characteristics date type : ordinary day & holiday weather situation : rainstorms week type : Mon, Tue… temperature
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Similarity of different days
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Prediction algorithm
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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
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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.
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Experiment result Use load and weather data of a week in June 2008 in Hainan The historical days selected is 29 days (n=29)
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Experiment result
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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.
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