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24-Hour Load Forecasting For Anomalous Weather Days Using Hourly Temperature
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 Title & Authors
24-Hour Load Forecasting For Anomalous Weather Days Using Hourly Temperature
Kang, Dong-Ho; Park, Jeong-Do; Song, Kyung-Bin;
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Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.
Short-term load forecasting;Similar day;Hourly temperature;Anomalous weather days;
 Cited by
Suhartono, et. al., "Two-Level Seasonal Model Based on Hybrid ARIMA-ANFIS for Forecasting Short-Term Electricity Load in Indonesia", International Conference on Statistics in Science, Business and Engineering (ICSSBE), pp.1-5, 2012.

Zhang Xiaoyun, Wu Ying, "Load Forecasting Based on Wavelet Analysis Combined with the Fuzzy Support Vector Kernel Regression Method", International Conference on Electric Information and Control Engineering (ICEICE), pp.499-504, 2011.

Kenji Nose-Filho, Anna Diva Plasencia Lotufo, Carlos Roberto Minussi, "Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network", IEEE Trans. On Power Delivery, vol.26, no.4, pp.2862-2869, Oct. 2011. crossref(new window)

Siddharth Arora, James W. Taylor, "Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods", IEEE Trans. on Power Systems, vol.28, no.3, pp.3235-3242, Aug. 2013. crossref(new window)

V. H. Hinojosa, A. Hoese, "Short-Term Load Forecasting Using Fuzzy Inductive Reasoning and Evolutionary Algorithms", IEEE Trans. on Power Systems, vol.25, no.1, pp.565-574, Feb. 2010. crossref(new window)

KPX, "A Study on the New Load Forecasting System Development Based on the Analysis of Electrical Power and Weather Feature", 2014.

Korea Meteorological Administration, ""

KPX, "A Study on Short-Term Load Forecasting Technique and its Application", 2011.

Pang-Ning Tan, Michael Steinbach, Vipin Kumar, "Introduction To Data Mining", Addison Wesley, 2007.