Short Term Load Forecasting using an Artificial Neural Network

Transcription

1 Short Term Load Forecastng usng an Artfcal Neural Network D. Kown 1, M. Km 1, C. Hong 1,, S. Cho 2 1 Department of Computer Scence, Sangmyung Unversty, Seoul, Korea 2 Department of Energy Grd, Sangmyung Unversty, Seoul, Korea hongch@smu.ac.kr Abstract. The Artfcal Neural Network (ANN) has been wdely used for Short Term Load Forecastng (STLF). However, only usng the ANN wthout consderng of seasonal varables leads to naccuracy load demand predcton. Load forecastng hghly depends on temperature n summer and wnter season due demand of coolng and heatng equpment. So, ths paper suggests an hourly cumulatve temperature weght method usng lner regresson model and varaton weght of the temperature n order to mprove the predcton accuracy of short-term electrcty load. We have performed the experments wth the actual electrcty load data and weather factors. The expermental results show that the proposed model mproves the predcton accuracy of load demand forecastng through the summer season and wnter season. Keywords: Short-Term Load Forecastng, Artfcal Neural Networks, Back propagaton learnng. 1 Introducton Electrcty load forecastng [1, 2] provdes a crteron not only the market energy transacton plan but also long term power faclty plan such as electrc generator, power transmsson, and power transformer. It leads to stable electrc power supply and adjustment of demand supply, and reduce the economc loss through the optmzng load flow and reactve power management [3]. The Back Propagaton Learnng (BPL) [4] s well-known and commonly appled to forecast load demand. It s separated nto two phases whch are the propagaton and weght update. Frst, each nput nodes receve nput varables usng ntal weght whch s sendng to all hdden layer s nodes. Each hdden unt computes the weght and transfers t to the output node. Output node calculates the error wth desre output and error s propagated back. Prevous each hdden node updates the weghts that were propagated back [5]. Correspondng Author ITCS 2013, ASTL Vol. 25, pp , 2013 SERSC

2 Proceedngs, The 2nd Internatonal Conference on Informaton Technology and Computer Scence Ths paper s concerned wth the 24 hours load forecastng model. In order to forecast one day ahead of load demand, we use BPL algorthm. Ths paper proposes a STLF model usng cumulatve temperature weght method whch consders the lnear regresson and temperature varaton weght. 2 Input Varables Selecton Input varables selectng s very mportant to apply the multlayer feedforward neural network model for forecastng the load demand [6]. There s a hgh correlaton between load demand and three factors whch are categorzed as the weather factors, tme factors, and economc factors. Economc factors have relevance to load demand n terms of economc perspectve such as a rate of populaton growth or actvaton of economes [7, 8]. Also, weather factors (temperature, dew pont, humdty, etc.) and tme factors (season, holday, week pattern) are closely related wth short term load forecastng. Economc factors are much more dffcult to apply for load forecastng model because of complcated statstcal methods whch need a lot of tme and efforts. So, we choose the average growth rate of the load demand whch should be easly reflect a rate of economc growth and populaton [9]. Tme and weather factors are selected from the past 3 years data between 2010 and The tme factors are selected from the weekly load demand patterns and weather factors are pcked from correlaton among the weather elements. In order to measure a correlaton, we used the person correlaton coeffcent method and results can be shown as Table 1 [10]. Table 1. Correlaton Coeffcent n the Summer and Wnter seasons. Summer season load temp dew hum hpa wn load 1 temp dew hum hpa wn Wnter season load temp dew hum hpa wn load 1 temp dew hum hpa wn Table 1 shows the correlaton coeffcent between hstorcal load demand and each weather varable. The correlaton between temperature and hstorcal load for the summer season has value and the next largest value s the dew pont whch has value Also, n the wnter season, the correlaton between temperature and hstorcal load s as the most correlated parameter and as the second, dew pont s In the summer season, the load demand strongly depends on the temperature than the wnter season. Other weather factors are neglgble on the load demand because t has meanngless correlaton coeffcent. 350

3 Short Term Load Forecastng usng an Artfcal Neural Network The nput load values are conssted of hourly load data of three days whch are prevous two days and one week before the day to be predcted. Equaton (1) denotes the nput load varable Load nput, d s a day, and t s a hour. Load nput = L(d-1,t) + L(d-2,t) +L(d-7,t) (1) 3 Temperature Weght Generaton In order to mprove the accuracy of short term load forecastng, we use the weght value of the temperature n every hours. The weght value of temperature s composed of two parts whch are the Polynomal Regresson curve and varaton of the weather elements data. Lnear regresson equatons of the electrcty load demand are representng the temperature response n the summer and wnter seasons. The regresson equatons are gven by: Y s = X X X (2) Y w = x x x x (3) The equaton Y s represents the summer regresson and the equaton Y w represents the wnter regresson. In addton, varaton of temperature s measured by the equaton (4). ΔW s the varaton between one day ahead forecast temperature and present temperature. ΔW T T (4) pre In the summer, f the temperature weght ( ΔW ) s ncreased, the weght value should be ncreased. On the contrary, f ΔW s ncreased n the wnter season, t lead to decrease the weght value because ncreased temperature reduces the heatng load demand. The temperature weght s appled to the proposed model usng the equaton (5). If season == summer W = Y + ΔW s Else If season == wnter W = Y ΔW w The Weght ( W ) s appled to the load predcton data (6). (5) 351

4 Proceedngs, The 2nd Internatonal Conference on Informaton Technology and Computer Scence L = L + ( L W ) (6) L s the load predcton data whch s calculated by BPL algorthm. 4 Experments and Results The proposed model derved the nput varables from the hstorcal hourly dataset of the U.S. State of Connectcut durng the years and t s tested on the summer and wnter season 2010 wth the actual data. In order to evaluate the performance of the load forecastng model, the mean absolute percentage error (MAPE) s consdered to measure the accuracy of the load forecast performance between the actual load data and the forecasted load data [11, 12]. The MAPE s defned as follows (7) MAPE 1 N n n 1 Y X Y 100 (7) Y s the actual load data, and X s the forecasted load data. Fg. 1 shows the results of the one day ahead load predcton n the summer and wnter seasons. Fg. 1. Load forecastng result of the summer and wnter seasons Table. 2 presents the result of MAPE appled the temperature weght ( W ) for 7 days, t shows that usng the temperature weght brngs out more accuracy forecastng. In 352

5 Short Term Load Forecastng usng an Artfcal Neural Network the summer season, the error percentage was reduced about 56% and n wnter season error percentage was reduced about 86%. Table 2. The result of the MAPE wth temperature weght ( W ). Season Summer Wnter W excludng ncludng excludng ncludng 0 ~ 24h ~ 48h ~ 72h MAPE 72 ~ 96h ~ 120h ~ 144h ~ 168h Average Conclusons The proposed model used the correlaton coeffcent to recognze the sgnfcant weather factors and pck out the meanngful factor such as temperature factor. The temperature factor was used to obtan the temperate weght value. Each season has dfferent pattern of the load demand that s owng to weather changng. Especally, temperature s the most mpact factor. So, temperature needs to be consdered as the prmary seasonal pattern element. Ths paper has proposed the hourly load forecast of a day ahead usng BPL algorthm. The smulaton on the actual load demand has shown the mprovement of load forecastng performance n both summer and wnter seasons by applyng the cumulatve temperature weght value. Acknowledgments. Ths research was supported by a grant (11 Hgh-tech Urban G07) from Hgh-tech Urban Development Program funded by Mnstry of Land, Infrastructure and Transport of Korean government. References 1. Km, K.H., Park, J.K., Hwang, K.J.: A Hybrd Short-term Load Forecastng Model usng Artfcal Neural Networks and Fuzzy Expert Systems (n Korean). The Transactons of the Korean Insttute of Electrcal Engneers. 43(12), (1994) 2. Ortz-Arroyo, D., Skov, M.K., Huynh, Q.: Accurate Electrcty Load Forecastng wth Artfcal Neural Networks. In: Proceedngs of the Internatonal Conference on Computatonal Intellgence for Modelng, Control and Automaton and Internatonal Conference on Intellgent Agents, Web Technologes and Internet Commerce, pp IEEE Press, Venna (2005) 3. Charytonuk, W., Chen, M.S.: Neural network desgn for short-term load forecastng. In: Proceedngs of the Internatonal Conference on Electrc Utlty Deregulaton and Restructurng and Power Technologes, pp London (2000) 353

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