A learning framework based on weighted knowledge transfer for holiday load forecasting
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摘要
Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays.First,we select source cities which can provide extra hidden knowledge to improve the forecast accuracy of the load of the target city. Then, all the instances which are from source cities and the target city will be weighted and trained by the improved weighted transfer learning algorithm which is based on the TrAdaBoost algorithm and can decrease negative transfer. We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities in Guangdong province of China,to illustrate the performance of the method. To solve the problem of limited historical holiday load data, we transfer the data from nearby cities based on the fact that nearby cities in Guangdong province of China have a similar economic development level and similar load variation pattern. The results of comparative experiments show that the framework proposed by this paper outperforms these methods in terms of mean absolute percent error and mean absolute scaled error.
Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays.First,we select source cities which can provide extra hidden knowledge to improve the forecast accuracy of the load of the target city. Then, all the instances which are from source cities and the target city will be weighted and trained by the improved weighted transfer learning algorithm which is based on the TrAdaBoost algorithm and can decrease negative transfer. We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities in Guangdong province of China,to illustrate the performance of the method. To solve the problem of limited historical holiday load data, we transfer the data from nearby cities based on the fact that nearby cities in Guangdong province of China have a similar economic development level and similar load variation pattern. The results of comparative experiments show that the framework proposed by this paper outperforms these methods in terms of mean absolute percent error and mean absolute scaled error.
Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays.First,we select source cities which can provide extra hidden knowledge to improve the forecast accuracy of the load of the target city. Then, all the instances which are from source cities and the target city will be weighted and trained by the improved weighted transfer learning algorithm which is based on the TrAdaBoost algorithm and can decrease negative transfer. We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities in Guangdong province of China,to illustrate the performance of the method. To solve the problem of limited historical holiday load data, we transfer the data from nearby cities based on the fact that nearby cities in Guangdong province of China have a similar economic development level and similar load variation pattern. The results of comparative experiments show that the framework proposed by this paper outperforms these methods in terms of mean absolute percent error and mean absolute scaled error.
引文
[1] Hong T(2010)Short term electric load forecasting. https://search.proquest.com/docview/852985857.Accessed January 2010
[2] Hong T,Fan S(2016)Probabilistic electric load forecasting:a tutorial review.Int J Forecast 32(3):914-938
[3] Li YY,Han D,Yan Z(2018)Long-term system load forecasting based on data-driven linear clustering method. J Mod Power Syst Clean Energy 6(2):306-316
[4] Jin M,Zhou X,Zhang ZM et al(2012)Short-term power load forecasting using grey correlation contest modeling.Expert Syst Appl 39(1):773-779
[5] Song KB, Baek YS, Hong DH et al(2005)Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Trans Power Syst 20(1):96-101
[6] Zhou M, Jin M(2019)Holographic ensemble forecasting method for short-term power load. IEEE Trans Smart Grid10(1):425-434
[7] Rana M, Koprinska I(2016)Forecasting electricity load with advanced wavelet neural networks. N eurocomputing182:118-132
[8] Khotanzad A, Afkhami-Rohani R, Maratukulam D(1998)ANNSTLF-artificial neural network short-term load forecastergeneration three.IEEE Trans Power Syst 13(4):1413-1422
[9] Ceperic E, Ceperic V,Baric A(2013)A strategy for short-term load forecasting by support vector regression machines. IEEE Trans Power Syst 28(4):4356-4364
[10] Zeng P, Jin M(2018)Peak load forecasting based on multisource data and day-to-day topological network. IET Gener Transm Dis 12(6):1374-1381
[11] Ko CN,Lee CM(2013)Short-term load forecasting using SVR(support vector regression)-based radial basis function neural network with dual extended Kalman filter.Energy 49:413-422
[12] Lou CW, Bong MC(2015)A novel random fuzzy neural networks for tackling uncertainties of electric load forecasting.Int J Electr Power 73:34-44
[13] Patil MA, Tagade P, Hariharan KS et al(2015)A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation. Appl Energy 159:285-297
[14] Zhu L, Li MS, Wu QH et al(2015)Short-term natural gas demand predic tion based on support vector regression with false neighbours filtered. Energy 80:428-436
[15] Chen BJ, Chang MW, Lin CJ(2004)Load forecasting using support vector machines:a study on EUNITE competition 2001.IEEE Trans Power Syst 19(4):1821-1830
[16] Elattar EE, Goulermas J, Wu QH(2010)Electric load forecasting based on locally weighted support vector regression.IEEE Trans Syst Man Cybern C 40(4):438-447
[17] Hu ZY, Bao YK, Xiong T(2014)Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression.Appl Soft Comput 25:15-25
[18] Che JX, Wang JZ(2014)Short-term load forecasting using a kernel-based support vector regression combination model.Appl Energy 132:602-609
[19] Xie JR,Hong T(2018)Load forecasting using 24 solar terms.J Mod Power Syst Clean Energy 6(2):208-214
[20] Hong T, Pinson P, Fan S(2014)Global energy forecasting competition 2012.Int J Forecast 30(2):357-363
[21] Charlton N,Singleton C(2014)A refined parametric model for short term load forecasting.Int J Forecast 30(2):364-368
[22] Ben Taieb S,Hyndman RJ(2014)A gradient boosting approach to the Kaggle load forecasting competition. Int J Forecast30(2):382-394
[23] Ziel F(2018)Modeling public holidays in load forecasting:a German case study. J Mod Power Syst Clean Energy6(2):191-207
[24] Xie J, Chien A(2016)Holiday demand forecasting in the electric utility industry.In:Proceedings of the 2016 SAS global forum,Las Vegas,USA, 18-21 April 2016, 13 pp
[25] Zhang YL, Luo GM(2015)Short term power load prediction with knowledge transfer.Inf Syst 53:161-169
[26] Zhao H, Min F, Zhu W(2013)Test-cost-sensitive attribute reduction of data with normal distribution measurement errors.Math Probl Eng 3:4928-4942
[27] Hu ZY, Bao YK, Xiong T et al(2015)Hybrid filter-wrapper feature selection for short-term load forecasting.Eng Appl Artif Intell 40:17-27
[28] Koprinska I, Rana M, Agelidis VG(2015)Correlation and instance based feature selection for electricity load forecasting.Knowl Based Syst 82:29-40
[29] Che JX,Wang JZ,Tang YJ(2012)Optimal training subset in a support vector regression electric load forecasting model. Appl Soft Comput 12(5):1523-1531
[30] Dai W, Yang Q, Xue GR et al(2007)Boosting for transfer learning.In:Proceedings of the 24th international conference on machine learning, Corvallis, USA, 20-24 June 2007,pp 193-200
[2] Hong T,Fan S(2016)Probabilistic electric load forecasting:a tutorial review.Int J Forecast 32(3):914-938
[3] Li YY,Han D,Yan Z(2018)Long-term system load forecasting based on data-driven linear clustering method. J Mod Power Syst Clean Energy 6(2):306-316
[4] Jin M,Zhou X,Zhang ZM et al(2012)Short-term power load forecasting using grey correlation contest modeling.Expert Syst Appl 39(1):773-779
[5] Song KB, Baek YS, Hong DH et al(2005)Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Trans Power Syst 20(1):96-101
[6] Zhou M, Jin M(2019)Holographic ensemble forecasting method for short-term power load. IEEE Trans Smart Grid10(1):425-434
[7] Rana M, Koprinska I(2016)Forecasting electricity load with advanced wavelet neural networks. N eurocomputing182:118-132
[8] Khotanzad A, Afkhami-Rohani R, Maratukulam D(1998)ANNSTLF-artificial neural network short-term load forecastergeneration three.IEEE Trans Power Syst 13(4):1413-1422
[9] Ceperic E, Ceperic V,Baric A(2013)A strategy for short-term load forecasting by support vector regression machines. IEEE Trans Power Syst 28(4):4356-4364
[10] Zeng P, Jin M(2018)Peak load forecasting based on multisource data and day-to-day topological network. IET Gener Transm Dis 12(6):1374-1381
[11] Ko CN,Lee CM(2013)Short-term load forecasting using SVR(support vector regression)-based radial basis function neural network with dual extended Kalman filter.Energy 49:413-422
[12] Lou CW, Bong MC(2015)A novel random fuzzy neural networks for tackling uncertainties of electric load forecasting.Int J Electr Power 73:34-44
[13] Patil MA, Tagade P, Hariharan KS et al(2015)A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation. Appl Energy 159:285-297
[14] Zhu L, Li MS, Wu QH et al(2015)Short-term natural gas demand predic tion based on support vector regression with false neighbours filtered. Energy 80:428-436
[15] Chen BJ, Chang MW, Lin CJ(2004)Load forecasting using support vector machines:a study on EUNITE competition 2001.IEEE Trans Power Syst 19(4):1821-1830
[16] Elattar EE, Goulermas J, Wu QH(2010)Electric load forecasting based on locally weighted support vector regression.IEEE Trans Syst Man Cybern C 40(4):438-447
[17] Hu ZY, Bao YK, Xiong T(2014)Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression.Appl Soft Comput 25:15-25
[18] Che JX, Wang JZ(2014)Short-term load forecasting using a kernel-based support vector regression combination model.Appl Energy 132:602-609
[19] Xie JR,Hong T(2018)Load forecasting using 24 solar terms.J Mod Power Syst Clean Energy 6(2):208-214
[20] Hong T, Pinson P, Fan S(2014)Global energy forecasting competition 2012.Int J Forecast 30(2):357-363
[21] Charlton N,Singleton C(2014)A refined parametric model for short term load forecasting.Int J Forecast 30(2):364-368
[22] Ben Taieb S,Hyndman RJ(2014)A gradient boosting approach to the Kaggle load forecasting competition. Int J Forecast30(2):382-394
[23] Ziel F(2018)Modeling public holidays in load forecasting:a German case study. J Mod Power Syst Clean Energy6(2):191-207
[24] Xie J, Chien A(2016)Holiday demand forecasting in the electric utility industry.In:Proceedings of the 2016 SAS global forum,Las Vegas,USA, 18-21 April 2016, 13 pp
[25] Zhang YL, Luo GM(2015)Short term power load prediction with knowledge transfer.Inf Syst 53:161-169
[26] Zhao H, Min F, Zhu W(2013)Test-cost-sensitive attribute reduction of data with normal distribution measurement errors.Math Probl Eng 3:4928-4942
[27] Hu ZY, Bao YK, Xiong T et al(2015)Hybrid filter-wrapper feature selection for short-term load forecasting.Eng Appl Artif Intell 40:17-27
[28] Koprinska I, Rana M, Agelidis VG(2015)Correlation and instance based feature selection for electricity load forecasting.Knowl Based Syst 82:29-40
[29] Che JX,Wang JZ,Tang YJ(2012)Optimal training subset in a support vector regression electric load forecasting model. Appl Soft Comput 12(5):1523-1531
[30] Dai W, Yang Q, Xue GR et al(2007)Boosting for transfer learning.In:Proceedings of the 24th international conference on machine learning, Corvallis, USA, 20-24 June 2007,pp 193-200