Cloud Computing for Short-Term Load Forecasting Based on Machine Learning Technique

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1 Internatonal Journal of Appled Engneerng Research ISS Volume 1, umber 19 (017) pp Research Inda Publcatons. Cloud Computng for Short-Term Load Forecastng Based on Machne Learnng Technque Tamer A. Farrag 1, and Ehab. E. Elattar 1,3 1 Department of Electrcal Engneerng, Faculty of Engneerng, Taf Unversty, KSA. Computers Engneerng Department, Msr Hgher Insttute of Engneerng and Technology, Egypt. 3 Department of Electrcal Engneerng, Faculty of Engneerng, Menofa Unversty, Egypt. Orcd IDs : X, Abstract Short-term electrc load forecastng (STLF) plays the man role n makng operatonal decsons n any electrcal power system. The mplementaton of forecastng algorthms colldes wth the hgh computatonal power needed to perform the complex perdton processes on large datasets. In ths paper, a cloudbased STLF algorthm s mplemented. The performance analyss of the proposed system was compared aganst the mplementaton of the same algorthm on a local machne and aganst many other forecastng algorthms. The results show that the cloud-based mplementaton enhances the algorthm executon tme. Keywords: Load Forecastng; SVR; Cloud Computng; Azure ML ITRODUCTIO Electrc load forecastng has always been a vtal part of an effcent power system plannng and operaton wth the ntroducton of deregulaton nto power ndustry. The fundamental objectve of the electrc power ndustry s to maxmze the effcency of electrcty generaton and consumpton and reduce energy prces [1]. Short-term electrc load forecastng (STLF) s mperatve for makng operatonal decsons n electrc power systems, such as unt commtment, reducng spnnng reserve, economc dspatch, relablty, analyss, and mantenance schedulng []. STLF deals wth load forecastng from one hour up to a few days. Recently, accurate STLF has ganed more mportance and greater challenges n deregulaton of electrcty markets. STLF s stll a challenge because of ts hgh complexty. A number of models were proposed durng the past few decades. These can be classfed as ether tradtonal or machne learnng based technques. The former nclude tme seres predctors such as autoregressve movng average exogenous varable (ARMAX) model[3]. These methods are based on a lnear regresson model and cannot always represent the nonlnear characterstcs of complex loads[4]. Dfferent machne learnng technques were used for STLF, such as artfcal neural networks (As) [5], radal bass functon (RBF) [6], fuzzyneural models [7] and support vector regresson[8]. The forecastng process depends on analyzng the hstorcal data of electrcty demands n a partcular country or regon, and many other factors can be consdered such as weather forecastng and commercal plans. Therefore, the entre data hstory s needed to tran the predcton model, but such an approach has the dsadvantage that f new nformaton s taken nto consderaton then all the parameters of the model may need to be retraned, and also a long parameter re-estmaton stage s requred. In addton, ths huge amount of data and the expected complex forecastng process lead to the need for massve computatonal power. The researchers tred to fnd approxmaton methods to mnmze ths amount of data and to mnmze the needed computatonal power. They try to exclude some factors, to apply samplng technques and so on. Many approaches solved these backdraws. One of them employed the local predctors as n[9][10]. In ths work, we wll beneft from Cloud Computng (CC), the new revoluton n the feld of computng systems. Economc constrants play the man role n any appled research. The appearng of CC paradgm solves many economc ssues that face researchers and developers. Before CC, the supercomputer was the only approprate choce to get a huge computatonal power whch was a very expensve choce. There are many computng systems such as dsturbed systems, grd computng, nternet computng, quantum computng etc., but CC s the most applcable commercal system. CC works accordng to three servce models: Software as a Servce (SaaS), Platform as a Servce (PaaS) and Infrastructure as a Servce (IaaS). owadays, many leadng companes provde CC servces to publc, and they attract a huge number of clents. The most mportant CC servces provders lst ncludes Mcrosoft Azure[11], Amazon Web Servce (AWS)[1] and Google App Engne[13]. The rest of ths paper s organzed as follow: Secton 0, the problem defnton and proposed soluton are explaned. In Secton 0, the desgn of experments s presented. Experments results and dscusson about them s ntroduced n secton 0. Fnally, the paper s concluded n secton

2 Internatonal Journal of Appled Engneerng Research ISS Volume 1, umber 19 (017) pp Research Inda Publcatons. SHORT TERM LOAD FORECASTIG BASED O SUPPORT VECTOR REGRESSIO owadays, there s no electrc utlty that can operate n an economcal, secure and relable mode wthout STLF. Forecastng tools wth hgher accuracy are essental to get lower operatng costs. Overpredcton of STLF wastes resources snce more reserves are avalable than needed and, n turn, ncreases the operatng cost. On the other hand, underpredcton of STLF leads to a falure to provde the necessary reserves whch also ncreases the operatng cost [15]. In general, the electrcal load s composed of dfferent consumpton unts. Varous factors affect the changes of ths electrcal load. These factors can be classfed as weather, calendar, economcal, and random factors. STLF can be consdered as a multvarable predcton problem. It can be solved as a functon of regresson problem. The next day load s the output of regresson model, whle the hstorcal load data and ts nfluencng factors are the nput data of regresson model. Hstory database supples the tranng data. The fnal goal of ths regresson problem s to fnd a mappng functon from hstorcal load data and ts nfluencng factors to the forecasted load wth good generalzaton ablty. The hstorcal load data s dvded nto two dfferent data sets. The frst one s the tranng data set whch s used to tran the regresson model, whle the other one s the testng data set whch s used to evaluate the traned regresson model [16]. Support vector regresson (SVR)[17] [18] whch proposed based on statstcal learnng theory (SLT), has been nvestgated as a promsng approach to power load forecastng [19]. Its advantages manly come from the employng of structural rsk mnmzaton prncple as an alternatve of emprcal rsk mnmzaton prncple Consequently, t can obtan an optmal global soluton by solvng a quadratc problem. There are two man features n the executon of SVR. They are quadratc programmng and kernel functons.a quadratc programmng problem wll be solved wth lnear constrants to get the SVR s parameters. The flexblty of kernel functons lets the technque to search a broad range of the soluton space [18]. The prmary objectve of SVR s to map the data x nto a hgh dmensonal feature space va a nonlnear mappng, and perform a lnear regresson n that feature space as [17] [18]: f(x) = w, x + b (1) Where,.,. represents the dot product, w contans the coeffcents that have to be estmated from the data and b s a real constant. Usng Vapnk s ε nsenstve loss functon [18], the overall optmzaton s formulated as: mn w, b,, 1 subject to T w w C 1 T w x x b y T w, 0, b y 1,..., where x s mapped to hgher dmensonal space by the functon, ε s a real constant, and are slack varables subject to ε- nsenstve zone and the regularzaton parameter (C) determnes the trade-off between the flatness of f and tranng errors. Introducng Lagrange multplers and wth 0 and, 0 for =1,,, and accordng to the Karush-Kuhn- Tucker optmalty condtons [18], the SVR tranng procedure amounts to solvng the convex quadratc problem: K x x 1 mn j j,,, j1 y 1 1 0, C subject to 1 0, 1,..., The output s a unque global optmzed result that has the form: yˆ fˆ x Q x, x j () (3) b (4) 1 Where, Q(x,x) s a kernel functon. By employng kernels n SVR, all necessary computatons can be calculated drectly n the nput space. Varous kernels exst such as lnear, hyperbolc tangent, Gaussan Radal bass functon, polynomal, etc. [18]. Here, we used the commonly used RBF kernel as follows: Q(x, x) = e γ x x (5) The SVR s parameters (C, γ, and ε) play a vtal role n the performance of SVR. So, choosng the proper values of these parameters leads to mnmze the forecastng error as shown next n ths paper. Kernelzed SVRs requre the computaton of a dstance functon between each pont n the dataset, whch s the domnatng cost of: O(n features n observatons ) (6) Solvng the STLF problem based on SVR can be summarzed n the followng steps: Frst step: The hstorcal data s loaded and dvded nto a tranng set and valdaton set. Second step: The parameters of SVR are determned accurately. 8690

3 Internatonal Journal of Appled Engneerng Research ISS Volume 1, umber 19 (017) pp Research Inda Publcatons. Thrd step: The SVR s traned usng the defned parameters to get the support vectors and correspondng coeffcents. Fourth step: Then the forecasted load can be obtaned usng (4). usng MATLAB. The desktop computer has the followng specfcatons: Processor Mcrosoft Wndows 10, Intel Core 7.7GHz, RAM 8GB. For the cloud-based mplementaton, Azure ML s used. EXPERIMETS The man purpose of ths research s to study the mpact of applyng cloud paradgm n the feld of electrcal load forecastng. The experments are desgned to test two ponts. The frst pont s the accuracy of mplementng load forecastng technques usng Azure ML. The second and the most mportant pont s to test the mprovement n the executon tme. Performance Metrcs In ths research, two man performance metrcs s consdered. The frst and the most mportant one s the executon tme (T Executon ). The second one s the predcton accuracy. All the experments wll be evaluated the predcton accuracy usng four metrcs: mean absolute percentage error (MAPE), the magntude of maxmum error (MAX), mean-absolute error (MAE) and normalzed mean-square error (MSE). These values are defned by the followng equatons: Dataset The dataset s used to verfy the applyng of cloud-based tools s that provded by European etwork on Intellgent Technologes for Smart Adaptve Systems (EUITE) competton [0]. The organzers of the competton had provded the half hourly electrcty load demand from January 1997 to December 1998, average daly temperature from 1995 to 1998, and holday s nformaton from 1997 to The results of more than 5 compettors are avalable. The goal of the competton s to forecast the daly maxmum load n January The real values of loads n January 1999 are avalable to compute the effcency of the forecastng algorthm. Implementaton The mplementaton of the desgned experments has two choces, local mplementaton, and cloud-based mplementaton. For the local mplementaton, a desktop computer s used, and the proposed algorthm s mplemented MAPE = 1 A P (7) 100 =1 A MAX = max( A P ) (8) MAE = 1 A P =1 (9) Δ = 1 1 (A A ) =1 1 MSE = Δ (A P ) =1 (10) (11) where A, P, A and s the actual value, the predcted value, the mean of the actual values and the testng dataset sze respectvely. RESULTS AD DISCUSSIO As stated before, there are a dataset was used n ths research: EUITE. For the used dataset, there were a number of experments scenaros were examned to compare and evaluate the proposed cloud-based SVR algorthm for STLF aganst the other technques and aganst local mplementaton usng MATLAB and the personal PC. Table 1: Experments scenaros of EUITE Dataset. Experment code Dataset Tranng set Testng set crtera sze crtera sze EUITE1 EUITE Daly Load only All months n 1997 and EUITE EUITE Daly Load + Temp All months n 1997 and EUITE3 EUITE Daly Load only Wnter months (10, 11, 1,1,, 3) n 1997 and EUITE4 EUITE Daly Load + Temp. Wnter months (10, 11,1,1,,3) n 1997 and January January January January

4 Executon Tme (seconds) Internatonal Journal of Appled Engneerng Research ISS Volume 1, umber 19 (017) pp Research Inda Publcatons. Four dfferent experment scenaros were mplemented. The target of all of these scenaros was to tran the machne learnng model usng the tranng set and to use ths traned model to predct the maxmum daly load expected n 31 days n January The dfference among the proposed experments scenaros was the crtera of choosng the tranng set. Table 1 summarzes the metadata of the proposed experments scenaros. Table : Results of EUITE experments. Experment code EUITE1 677 EUITE 75 EUITE3 101 EUITE4 10 T Executon MAPE MAE MAX MSE Parameters Local Cloud % (MW) γ C Table, Fgure 1 and Fgure show the results of executng the EUITE experments for STLF. The results show that the executon tme n the case of usng the cloud was much less that usng the local machne. Ths s observed easly n case of experment "EUITE" because of the relatvely bg number of tranng data (73 4 (dmenson of the tme seres) ( load +temp) = 5784 value) refer to equaton (6). In EUITE, the mprovement n tme executon was more than 10 tmes n the cloud case vs. the local machne case. It s worth to state that the second run of the experments on the cloud usng Azure Ml studo was faster than the frst run, for example, n EUITE the executon tme equals 9 seconds nstead of 7 seconds. In addton, the results show that the best predcton accuracy was acheved n the case of "EUITE4" where MAPE=.04%. Fgure 3 shows the values of MAPE and MAX for compettors n EUITE competton compared to the result of "EUITE4" experment. As shown, ths s the second best result when compared to the EUITE competton results Local Cloud EUITE1 EUITE EUITE3 EUITE4 Fgure 1: Executon tme of EUITE experments n the cloud vs. local machne. 869

5 MAPE [%] MAX [MW] Internatonal Journal of Appled Engneerng Research ISS Volume 1, umber 19 (017) pp Research Inda Publcatons. Fgure : Predcted daly electrcal load vs. the actual reads of Jan for EUITE experments. 16 MAPE[%] Max [MW] Fgure 3: Results of ths work compared to results of the 001 EUITE competton ndcatng MAPE and MAX metrcs [0]. 8693

6 Internatonal Journal of Appled Engneerng Research ISS Volume 1, umber 19 (017) pp Research Inda Publcatons. COCLUSIO In ths paper, A cloud base mplementaton for STLF usng SVR s presented. The tme of executon and the predcton accuracy are the performance measures. The forecastng algorthm ntroduced n [9] s mplemented locally and on the cloud. For the used dataset, the predcton accuracy for the two approaches was dentcal. But, the enhancement n the executon tme s remarkable n the case of cloud mplementaton. Tradtonally, SVR s not recommended for usng wth the large datasets because of ts hgh computatonal cost as shown n equaton (6). So, ths was a good exercse for the cloud mplementaton. ACKOWLEDGMET The authors gratefully acknowledge the Tal Unversty for ts support to carry out ths work. It funded ths project wth a fund number REFERECES [1] Askarpour, M., and V. Zenadn. "Securty-Constraned unt commtment reacton to load and prce forecastng errors." 6th Internatonal Conference on the European Energy Market, 009, pp. 1 7., do: /eem [] Kyrakdes, Elas, and Maros Polycarpou. "Short Term Electrc Load Forecastng: A Tutoral." Studes n Computatonal Intellgence Trends n eural Computaton, vol. 35, pp , do: / _16. [3] Huang, C.-M., et al. "A Partcle Swarm Optmzaton to Identfyng the ARMAX Model for Short-Term Load Forecastng." IEEE Transactons on Power Systems, vol. 0, no., 005, pp , do: /tpwrs [4] Senjyu, T., et al. "One-Hour-Ahead load forecastng usng neural network." IEEE Transactons on Power Systems, vol. 17, no. 1, 00, pp , do: / [5] Hppert, H.s., et al. "eural networks for short-term load forecastng: a revew and evaluaton." IEEE Transactons on Power Systems, vol. 16, no. 1, 001, pp , do: / [6] Sheng, Sqng, and Cong Wang. "Integratng Radal Bass Functon eural etwork wth Fuzzy Control for Load Forecastng n Power System." 005 IEEE/PES Transmsson &Amp; Dstrbuton Conference &Amp; Exposton: Asa and Pacfc, pp. 1 5., do: /tdc [7] Lng, S.h., et al. "Short-Term electrc load forecastng based on a neural fuzzy network." IEEE Transactons on Industral Electroncs, vol. 50, no. 6, 003, pp , do: /te [8] Zhang, Mng-Guang. "Short-Term load forecastng based on support vector machnes regresson." 005 Internatonal Conference on Machne Learnng and Cybernetcs, 005, do: /cmlc [9] El-Attar, E. E., et al. "Forecastng electrc daly peak load based on local predcton." 009 IEEE Power & Energy Socety General Meetng, 009, pp. 1 6., do: /pes [10] Elattar, E. E., et al. "Integratng KPCA and locally weghted support vector regresson for short-term load forecastng." Melecon th IEEE Medterranean Electrotechncal Conference, 010, pp , do: /melcon [11] "Mcrosoft Azure: Cloud Computng Platform & Servces." [Onlne]. Avalable: azure.mcrosoft.com/enus/. Accessed 5 Apr [1] "Amazon Web Servces (AWS) - Cloud Computng Servces." [Onlne]. Avalable: aws.amazon.com/. Accessed 5 Apr [13] "Google App Engne" [Onlne]. Avalable: cloud.google.com/appengne/docs/. Accessed 5 Apr [14] Buyya, Rajkumar, et al. Masterng Cloud Computng: Foundatons and Applcatons Programmng. 1st ed., Elsever, Inc., 013. [15] E. Elattar and T. Farrag, "Securty Constraned Unt Commtment Based Load and Prce Forecastng Usng Evolutonary Optmzed LSVR." Internatonal Journal of Scentfc & Engneerng Research, V. 5, no. 7, pp , 014. [16] Sun, Changyn, and Dengca Gong. "Support Vector Machnes wth PSO Algorthm for Short-Term Load Forecastng." 006 IEEE Internatonal Conference on etworkng, Sensng and Control, pp , do: /cnsc [17] Vapnk, Vladmr aumovch. Statstcal learnng theory. ew York, Wley & Sons, [18] Smola, Alex J., and Bernhard Schölkopf. "A tutoral on support vector regresson." Statstcs and Computng, vol. 14, no. 3, 004, pp. 199., do:10.103/b:stco [19] Kavous-Fard, Abdollah, et al. "A new hybrd Modfed Frefly Algorthm and Support Vector Regresson model for accurate Short Term Load Forecastng." Expert Systems wth Applcatons, vol. 41, no. 13, 014, pp , do: /j.eswa

7 Internatonal Journal of Appled Engneerng Research ISS Volume 1, umber 19 (017) pp Research Inda Publcatons. [0] Chen, B.-J., et al. "Load Forecastng Usng Support Vector Machnes: A Study on EUITE Competton 001." IEEE Transactons on Power Systems, vol. 19, no. 4, 004, pp , do: /tpwrs [1] Takens, Flors. "Detectng strange attractors n turbulence." Lecture otes n Mathematcs Dynamcal Systems and Turbulence, Warwck 1980, 1981, pp , do: /bfb