Electricity consumption forecasting method based on MPSO-BP neural network model Youshan Zhang 1, 2,a, Liangdong Guo2, b,qi Li 3, c and Junhui Li2, d

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1 4th Iteratioal Coferece o Electrical & Electroics Egieerig ad Computer Sciece (ICEEECS 2016) Electricity cosumptio forecastig method based o eural etwork model Yousha Zhag 1, 2,a, Liagdog Guo2, b,qi Li 3, c ad Juhui Li2, d 1 Neuroimagig ad Iformatics, Uiversity of Souther Califoria, Los Ageles, CA, USA 2 School of Sciece, Uiversity of Sciece ad Techology Liaoig, Asha, , Chia 3 Departmet of Automatio, BOHAI Uiversity, Liaoig, Jizhou, , Chia a youshaz@usc.edu, b ldguo@ustl.edu.c, c liqibaohaiu@163.com, d @163.com Keywords: algorithm; Electricity cosumptio; Neural etwork; Forecastig model Abstract: This paper deals with the problem of the electricity cosumptio forecastig method. A (modified particle swarm optimizatio-back propagatio) eural etwork model is costructed based o the history data of a mieral compay of Asha i Chia. The simulatio showed that the covergece of the algorithm ad forecastig accuracy usig the obtaied model are better tha those of other traditioal oes, such as,pso, fuzzy eural etwork ad so o. The we predict the electricity cosumptio of each moth i 2017 based o the eural etwork model. 1 Itroductio Mieral compaies cosume large quatities of electricity i the processig of coal every day. The electricity cosumptio predictig system is always a importat part of plaig ad operatig of the power. Because of the complicated chage of the electrical power system, it is difficult to establish a exact predictig model [1]. May compaies have chaged the traditioal methods to predict the electricity cosumptio, but the accuracy is ot high. Traditioal eural etwork traiig algorithms are mostly based o the gradiet. The speed of etwork learig process covergece is slow ad falls ito the local miimum easily. It is also difficult to decide the umber of euros i the hidde layer. I terms of the electric power loadig radomess, it lacks the ability of precise to scree data processig. The origial particle swarm optimizatio (PSO) has may advatages such as the simple algorithm, easily implemet ad less parameters. However, it has some disadvatages like is ot sesitive to the evirometal chages ad falls ito o-optimal regios easily [2-5]. I this paper, algorithm is modified to trai the eural etwork parameters, realize the optimizig of the etwork ad achieve the automatically optimized parameters of eural etwork. The algorithm is applied to predict the electricity cosumptio predictio by usig Matlab. I additio, our method is used to compare with methods of, PSO, Elma,, ad [6-10], the results show that our algorithm has a higher covergece speed, ad it provides a higher accuracy for predictig the electricity cosumptio. 2 Particle Swarm Optimizatio ad Its Improvemet 2.1 The origial particle swarm optimizatio I the PSO algorithm, each idividual is called a particle, ad each particle represets a potetial solutio. I the D-dimesioal search space, each particle is a poit i space ad group forms by m particles. z i (z i1,z i2, z id ) ad v i (v i1,v i2, v id,,v id ) are the positio vector ad the speed vector of i ( i1,2,,m) particle, p i (p i1,p i2, p id,,p id ) is the best positio of the search particle, p g (p g1,p g2, p gd,,p gd )is the best positio of all particles. The velocity ad positio updatig equatios: (1) vidk +1 vidk + c1r1( k ) ( pid - zidk ) + c2 r2(k) ( pgd - zidk ) +1 zidk zidk + vidk +1 (2) Where: k is the iteratio idex; r 1 ad r 2 are radom umbers amog [0, 1]; c 1 ad c 2 are the acceleratory coefficiet [7]. Copyright 2016, the Authors. Published by Atlatis Press. This is a ope access article uder the CC BY-NC licese ( 674

2 2.2 Modified particle swarm optimizatio Modified ideas: 1. Keepig flight diversity of later stage ad differet flight speed i the same directio. 2. Dividig particles ito two categories: high-speed particles satisfy the global search requiremets ad avoid premature ad local optimum; ad low-speed particles satisfy the refied search requiremets, ad avoid exceedig optimal solutio. The modified equatios are as follows: vidk +1 ω vidk + c1r1 ( pid - zidk ) + c2 r2 ( pgd - zidk ) +1 zidk zidk + vidk +1 0 ( )vid 1, 2,, j vid a (0) 1, 2,, j z z id id + zid (3) where: v id0 is a base part of the particle i i D-dimesioal velocity; vid( m ) is a part of the particle i i D-dimesioal search speed; zid(0) is a base part of the particle i i D-dimesio search positio; zid( m ) is a part of the particle i i D-dimesioal search positio; ω is iertia weight; P id is the best positio particle achieved based o its ow experiece; P gd is the best particle positio based o overall swarm s experiece. a ( ) / j 1 ± / j NI 2 vid(0) < N i 2 vid(0) N i1 < vid(0) < N i1 (4) a() is the coefficiet variatio of speed, N i1 is the maximum speed, N i2 is the miimum speed, a() chages the search speed accordig to the equatio (4). (5) ω ( k ) 2ω 0 / (1 + exp(σ k / k max ) Where: σ is the positive coefficiet; k max is the upper limit iteratio idex;ω0 is the upper limit ofω (k); k is the iteratio idex. 2.3 Performace aalysis after the improved algorithm The origial PSO algorithm caot keep the covergece of global optimum, ad the probability of gettig a optimal solutio is small. The modified PSO algorithm itroduces a speed variable coefficiets a(m) ad iertia factor ω. It keeps diversity of particle swarm, ad it ca obtai the global optimum ad improve the covergece speed ad accuracy. 3 Neural Network Algorithm The followig equatio ca be used to judge the fitess of particles: 1 m (6) f (d - t ) j 1 k 1 i k where d i is the actual output, t k is the target output, m is the umber of output odes, ad is the umber of traiig set samples. 3.1 Modified particle swarm optimizatio algorithm realizatio 1. Iitializatio: geeratig the positios ad velocities radomly to decide the local best positio (p id ) ad the global best positio (p gd ). The equatio (3) ad (4) decide the iitial parameters: σ,ω,ω0,c 1,c 2,r 1,r Evaluatio calculatig the particle fitess fuctio f accordig to the equatio (6) Comparig the best positio of each particle with the experiece positio of each particle, ad replacig the curret positio as the best positio if the curret positio is better tha the best positio, otherwise, the curret positio remais uchaged. 3. Update extreme : comparig the curret positio of each particle i the group with all the best positio experieced. If the positio of the particle is better, it will be settig to the best positio i the curret; otherwise, the positio will stay uchaged. 4. Update the iertia weight The iertia weight is updated accordig to the equatio (5). 5. Update the positio ad velocity of the particle: the positio ad velocity of the particle chaged by usig equatio (3) ad (4). 6. Check: If the curret accuracy ot achieves the preset accuracy or the umber of iteratios ot meets the ed coditios, tur back to step 2. [11] 675

3 After traiig the eural etwork, the electricity cosumptio ca be forecasted. 4 Neural Network ig Model 4.1 The predictio ad simulatio of electricity cosumptio Accordig to the history data of a mieral compay of Asha i Chia, the traiig samples of the eural etwork are the mothly electricity cosumptio history data from 2011 to I additio, the electricity cosumptio s of 12 moths i 2015 are treated as a test sample. By usig the traiig eural etwork, the iitial parameters are as follows: σ 0.8, ω0.7, ω00.9, c 1 2 ad c 2 2.4, the rage of r 1 ad r 2 is [0,1], α 0.07,η 0.80, M50, the maximum umber of iteratios is Differet algorithms were used to predict the electricity cosumptio of a mieral compay of Asha i 2015, the results are show as followig(due to the legth of the space, a part of the results are listed i Table 1) Methods True Moths Table 1. Eergy cosumptio predictio of each moth i 2015 Elma Methods MSE Average relative Relative Relative Value Relative Relative Value Relative Relative Value Table Electric eergy of each moth i 2015 Elma Fig. 1 Electricity cosumptio predictio Fig.2 The predictio It ca be see from table 1, table 2, figure 1, ad figure 2 that the predictig of the electricity cosumptio of algorithm is very close to the actual. Therefore, this method has a higher accuracy tha the others. Figure 3 ca also show that the predictig results of Elma ad algorithm are easy to appear oscillatig. However, predictig model has a fast traiig speed, high precisio, ad a good effect. The chagig of relative is also uiform, ad the chagig rage is betwee 0 with The aalysis of simulatio results I order to evaluate the effect of predictio model,,, Elma, ad predictig models are traied ad compared with the results of model. The results are show i Table 3: 676

4 Model Elma Table 3 Compariso of model traiig performace Accuracy Iteratios Average relative Maximum relative As ca be see from table 3, the ad the umber of iteratios of eural etwork are obviously less tha the traditioal,, Elma,, ad eural etworks. eural etwork has faster covergece, ad higher covergece precisio, so adoptig this optimizatio of the calculatio is feasible. Adoptig the traied eural etwork forecastig model, selected five moths radomly to predict the electricity cosumptio, ad the results show i Table 4: time Origial () ive () Accuracy () Table 4.Electricity cosumptio forecastig accuracy 8/ / /2013 4/ / It has show from the table 4, the predictig accuracy ca reach about 99, ad the highest predictig accuracy was Obviously, for multiple factors i the predictig of electricity cosumptio, such accuracy is difficult to achieve. It shows that the predictig model ca achieve high predictig accuracy ad high practicality. 4.3 The electricity cosumptio results Based o the eural etwork model, we predict the mothly electricity cosumptio of the mieral compay i 2017, the results are: 34.52, 36.32, 35.44, 36.01, 34.96, 35.62, 37.03, 35.15, 37.41, 35.92, 37.16, ad Coclusios I this paper, we proposed a modified eural etwork method to predict the electricity cosumptio. The eural etwork algorithm is used to trai the etwork, ad achieve the optimizatio of etwork parameter. This algorithm aalysis shows that the algorithm overcomes problems of eural etwork ad traditioal particle swarm optimizatio algorithm. This improved algorithm has a good predictive ability, improves the predictig accuracy ad provides a feasible ad practical method for forecastig electricity cosumptio. Refereces [1] Sejyu, T., Takara, H., Uezato, K., & Fuabashi, T. (2002). Oe-hour-ahead load forecastig usig eural etwork. IEEE Trasactios o Power Systems, 17(1), [2] Cheg, Z., & Jucheg, T. (2015). Adaptive combiatio forecastig model for chia's logistics freight volume based o a improved eural etwork. Kyberetes, 44(4), 646. [3] Jiag, X., Lig, H., Ya, J., Li, B., & Li, Z. (2013). Forecastig electrical eergy cosumptio of equipmet maiteace usig eural etwork ad particle swarm optimizatio. Mathematical Problems i Egieerig, 2013, 1-8. [4] Sethil Arumugam, M., Rao, M. V. C., & Chadramoha, A. (2008). A ew ad improved versio of particle swarm optimizatio algorithm with global local best parameters. Kowledge ad Iformatio Systems, 16(3), [5] Alais, A. Y., Ricalde, L. J., Simetti, C., & Odoe, F. (2013). Neural model with particle swarm optimizatio kalma learig for forecastig i smart grids. Mathematical Problems i Egieerig, 2013, 1-9. [6] Cheg, Z., & Jucheg, T. (2015). Adaptive combiatio forecastig model for chia's logistics freight volume based o a improved eural etwork. Kyberetes, 44(4), 646. [7] AlRashidi, M. R., & El-Hawary, M. E. (2009). A survey of particle swarm optimizatio applicatios i electric power systems. IEEE Trasactios o Evolutioary Computatio, 13(4), 677

5 [8] Li, P., Li, Y., Xiog, Q., Chai, Y., & Zhag, Y. (2014). Applicatio of a hybrid quatized elma eural etwork i short-term load forecastig. Iteratioal Joural of Electrical Power & Eergy Systems, 55, [9] Çevik, H. H., & Çukaş, M. (2015). Short-term load forecastig usig fuzzy logic ad. Neural Computig ad Applicatios, 26(6), [10] Paklib, K., Prakasvudhisar, C., & Khummogkol, D. (2015). Electricity cosumptio forecastig i thailad usig a artificial eural etwork ad multiple liear regressio. Eergy Sources, Part B: Ecoomics, Plaig, ad Policy, 10(4), [11] Shi B., Yu-Xia L I., Xi-Hua Y U. (2009). Short-term load forecast based o modified particle swarm optimizer ad back propagatio eural etwork model. Joural of Computer Applicatios, 29(4):