SHORT-TERM POWER FORECASTING BY STATISTICAL METHODS FOR PHOTOVOLTAIC PLANTS IN SOUTH ITALY

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1 4 th Imeko TC19 Symposum on Envronmental Instrumentaton and Measurements Protectng Envronment, Clmate Changes and Polluton Control June 3-4, 213, Lecce, Italy SHORT-TERM POWER FORECASTING BY STATISTICAL METHODS FOR PHOTOVOLTAIC PLANTS IN SOUTH ITALY Mara Graza De Gorg, Paolo Mara Congedo, Mara Malvon, Marco Tarantno Department of Engneerng for Innovaton, Unversty of Salento, Va per Monteron, 73 Lecce, Italy, Abstract: Statstcal methods based on Multregresson Analyss and Artfcal Neural Networks (ANNs) have been developed n order to predct power producton of a 96 kwp grd-connected photovoltac (PV) plant n the campus of the Unversty of Salento, Italy. The neural network has been used only as a statstc model based on tme seres of PV power and meteorologcal varables, as module temperature, ambent temperature and rradance on module s plan. In partcular, a senstvty analyss has been carred out n order to fnd those weather parameters wth the best mpact on the forecastng. Keywords: Forecastng, Photovoltac power, Artfcal neural networks, Predcton, Multregresson Analyss 1. INTRODUCTION An mportant ssue for the growth of PV sector and grd-connected photovoltac (PV) systems, s the forecastng of energy output throughout ts operaton. An optmal use of the renewable energy needs ts characterzaton and predcton n order to sze detectors or to estmate the potental of power plants. In terms of predcton, electrcty supplers are nterested n varous horzons to estmate the fossl fuel savng, to manage and dspatch the power plants nstalled [1] The uncertanty of power from the sun s a lmtaton of PV system, nfluencng the qualty of the electrcal system that connected. So, the possblty to predct the solar rradance (up to 24 h or even more) can became a very mportant role for an effcent plannng of the the Grd Connected photovoltac systems. In lterature dfferent forecastng methods have been developed to evaluate the performance of PV systems. In [2] C. Chupong and B. Plangklang presented the power forecastng of a PV system by calculatng the solar radaton, collectng data from weather forecastng, and usng Elman neural network to forecast by usng data from PV system. In [3] a MLP network for forecastng of 24 h ahead of solar rradance was developed. The proposed model used as nput parameters the mean daly rradance and the mean daly ar temperature. A good results were obtaned from comparson between the measured and the forecasted PV power. Statstcal predcton methods are based on models that establsh the relaton between hstorcal values of the power and the meteorologcal varables. So, t s mportant to choose the rght ambent data. ANNs are useful tools to understand the complex and nonlnear relatonshps among data, wthout any prevous assumpton concernng the nature of these correlatons. [4, 5]. The tranng s one of the most crtcal phase. In ths step the choce of nput data and of the neural connectons have to be properly set n order to have an approprate smulaton of the performance of a PV plant. An am of the present study s to underlne the nfluence of several weather parameters wth respect to the accuracy of PV power predctons. Ths paper presents an artfcal neural network (ANN) approach for forecastng the performance of electrc energy generated output from a 96 kwp grd-connected photovoltac (PV) plant nstalled n the campus of the Unversty of Salento, Italy. The present study s a part of the funded research project 7th Framework Programme Buldng Energy Advanced Management Systems (BEAMS). Part of the BEAMS research program s concernng the study on the benefts of nstallng PV systems and chargng statons for electrcal vehcles (EV) and the development of tools to mprove/optmze the dstrbuton of loads n the grd composed by the publc faclty servces. The Unversty of Salento s one of the two plot stes n whch ths project s beng developed. In the last 2 years, the unversty has sgnfcantly promoted the use of energy from renewable sources by the nstallaton of solar PV roofs on parkng areas and chargng statons for electrc cars. The ANN nterpolates among the solar PV generaton output and relevant parameters such as solar radaton, module temperature and ambent temperature Utlzng the regresson analyss, the nfluence of measured meteorologcal data on PV power generaton has been analyzed. In ths study, two ANN models are mplemented and valdated wth reasonable accuracy on real electrc energy generaton output data. In the frst model, the PV power output for the next 1 hour (t+1) s calculated, usng a tme seres of measured hourly data, ncluded the man parameter at tme t, module temperature, ambent temperature, rradance and nstant PV power. In the second approach, the PV power measure at the nstant t s ISBN:

2 mplemented to prevson PV power at t+1, wthout weather parameters. 2. HYSTORICAL DATA AND SITE DESCRIPTION The ste under study s the PV park, located n the campus of the Unversty of Salento, n Monteron d Lecce, Apula (4_19316N, 18_5544E). It s characterzed by a warm Medterranean clmate wth a dry summer. In order to defne a predcton model for PV power, the most sgnfcant problem remans the selecton of the best parameter to use from among the several varables of the system. A detaled descrpton of ths PV system s n [6]. The data acquston system conssts of three nverters, the solar rradance sensors and the PV module/ambent temperature sensors. The data from the nverters and the sensors are characterzed by protocols Modbus, Profbus, clean contacts or dgtal nputs, and they are collected by a PLC Semens wth a scada WINCC for processng and storage. In partcular, an analyss of the tme seres represented by the followng daly data (collected every 1 hour) has been carred out: module temperature ( C), ambent temperature ( C), rradance on plan nclned at a tlt angle of 3 and rradance for a tlt angle of 15 (W/m 2 ), PV power(w). The tme seres data used ncluded 365 days (from 5/3/212 to 5/3/213). 3.MULTIREGRESSION ANALYSIS Multple regresson s a data analyss technque that permts to measure of how well a gven parameter varable can be predcted usng a lnear functon of a set of other varables. The am of the mult-regresson analyss was to obtan a relatonshp between PV power, module temperature and the ambent condtons (ambent temperature and rradance on plan of modules). The frst effort made was to develop a model to predct PV power based on four nputs: ambent temperature, module temperature, rradance on two plans nclnated. The general form of the model equaton obtaned s: P = b 1 *T Amb + b 2 *T Mod +b 3 *I 3 + b 4 *I 15 The regresson coeffcents have been calculated by an teratvely reweghted least squares algorthm, wth the weghts at each teraton calculated by applyng the bsquare functon to the resduals from the prevous teraton.. Frst of all, a detaled senstvty analyss has been carred out n order to fnd those weather parameters wth the hghest mpact on the forecast by a lnear regresson between each weather parameter and the PV power. The best regresson for the nputs selecton could be evaluated n terms of squared correlaton coeffcent R 2. Fgures 1a-1d show the hourly PV power versus, respectvely, hourly ambent temperature, module temperature, rradance 3 and 15 on the bass of one year collected data. PV Output Power [W] Fg. 1.a R 2 coeffcent for lnear regresson between Ambent Temperature and PV Power PV Output Power [W] Fg. 1.b R 2 coeffcent for lnear regresson between Module Temperature and PV Power PV Output Power[W] PV Output Power[W] Fg. 1.c R 2 coeffcent for lnear regresson between Irradance on plan 3 and PV Power Fg.1.d R 2 coeffcent for lnear regresson between Irradance on plan 15 and PV Power It s evdent that the most correlated parameter wth PV power s gven by rradance. Table 1 Coeffcents for the four nput parameter T Mod Module Temperature T Amb Ambent Temperature I 3 Irradance on plane of module wth tlt 3 I 15 Irradance on plan of module wth tlt 15 b 1=.7 b 1=-.3 b 3=.39 b 4=.39 R² =, Ambent Temperature [ C] R² =, Module Temperature [ C] R² =, Irradance I 3 [W/m 2 ] R² =, Irradance I 15 [W/m 2 ] ISBN:

3 In vew of the R 2 values obtaned, all parameters have been taken nto consderaton to mplement the multregresson analyss. The coeffcents b 1, b 2, b 3, b 4 are presented n table 1. Fgure 2 shows coeffcent R 2 n the case of the mult-regresson analyss, underlnng good correlaton. Pretctons[W] R² =,977 network. All these neurons are hghly nterconnected and the actvaton values consttute fnal output or may be fed to the next model. These connecton weghts are contnuously modfed durng tranng to obtan desred accuracy and generalzaton capabltes. Elman ANN network In ths work, ELMAN ANN network have been used to forecastng and evaluatng the PV power of the park. Ths knd of network s characterzed by feedback from the frst layer output to the frst-layer nput. Ths recurrent connecton allows the Elman network to detect and generate tme-varyng patterns (Fg. 4). 7 8 Measures [W] Fg. 2. PV power measured versus values forecasted by mult-regresson analyss. 4. ARTIFICIAL NEURAL NETWORKS (ANNS) Neural networks are composed of smple elements operatng n parallel, nspred by bologcal nervous systems. The network functon s gven by the connectons between elements. A neural network s traned to perform a partcular functon by adjustng the values of the connectons (weghts) between elements (Fg.3). Fg.3 Basc Block Dagram of Neural Network The basc component of such a system s a neuron. When actve, electrochemcal sgnals are receved through synapses to the neuron cell. Each synapse has ts own weght that determnes the contrbuton and extent to whch the respectve nput affects the output of the neuron. The weghted sum of the nput electrochemcal sgnals s fed to the nucleus that sends electrcal mpulses n response, beng transmtted to other neurons or to other bologcal unts as actuaton sgnals. Neurons are nterconnected through synapses. The synaptc weghts modfy contnuously durng learnng. Groups of neurons are organzed nto subsystems and ntegrate to form the bran. In the ANN technque, a smulaton of a small part of the central nervous system s done whch s a rather basc mathematcal model of the bologcal nervous system. Inputs are fed nto the correspondng neurons, and the electrochemcal sgnals are altered by weghts. The weghted sum s operated upon by an actvaton functon, and outputs are fed to other neurons n the Fg.4 Typcal archtecture of an Elman Back Propagaton network. Results and dscusson In ths study, the ANN has been compled wth the Matlab software and ts Neural Network toolbox. Frstly, an accurate elaboraton of the measured values was necessary n order to check, n each month, the days n whch the parameters were ether unavalable or ncorrect. Subsequently, the real values of all data were normalzed n a range [-1, 1]. The neural network has been used only as a statstc model based on tme seres of on-lne measured PV power. For each tme nstant t, the nput value s gven by the average hourly power at that tme, whle the target s gven by the average hourly powers along the forecast horzon h=1. Table 2 shows the network parameters used n the tranng. As sad two dfferent ANN forecastng systems were mplemented. Table 3 descrbes the numercal parameters ncluded n each of the forecast systems. Table 2 Elman network parameters used n the tranng for the forecast system I and II Tranng functon TRAINGDX Adapt learnng functon LEARNGD Performance functon MSE Number layers 3 Neurons (layer 1) 5 Neurons (layer 2) 5 Neurons (layer 3) 1 Actvaton functon hdden layer TANSIG Actvaton functon output layer PURELIN Epochs ISBN:

4 Table 3 Numercal parameters ncluded n each of the forecast systems I II Forecast system Numercal parameters ncluded n the forecast system P PV output power at nstant t T Mod Module Temperature T Amb Ambent Temperature I 3 Irradance on plane of module wth tlt 3 I 15 Irradance on plan of module wth tlt 15 P PV output power at nstant t Model I s based on one nputs: the hourly average data of PV power and appled on a tranng perod of 1 years for a forecastng horzon at the tme t + 1 (1 h). The performance of the ANN s evaluated usng a data set of nput varables (testng data set) dfferent from that used n the tranng process. The testng data set s gven by the data collected n eght months, whle the tranng data s gven by the data collected n 3 months. All the collected data tme seres data (365 days/6297 hourly records) were dvded n two sets: tranng and testng data sets. The tranng data set ncluded 65% of the tme seres data, the testng data set 35%. These forecast values are compared wth the actual values recorded at ste (Fg.5). The second model s based on fve nputs: the hourly average data of the weather parameters and PV power and appled on a tranng perod of 1 years for a forecastng horzon at the tme t + 1 (1 h). 4 Power[W] Actual 2/1/13 4:25 AM 2/1/13 7:25 AM 2/1/13 1:25 AM 2/1/13 1:25 PM 2/1/13 4:25 PM 2/1/13 7:25 PM 2/11/13 4:35 AM 2/11/13 7:35 AM 2/11/13 1:35 AM 2/11/13 1:35 PM 2/11/13 4:35 PM 2/11/13 7:35 PM 2/12/13 4:45 AM 2/12/13 7:45 AM 2/12/13 1:45 AM 2/12/13 1:45 PM 2/12/13 4:45 PM 2/12/13 7:45 PM 2/13/13 4:55 AM 2/13/13 7:55 AM 2/13/13 1:55 AM 2/13/13 1:55 PM 2/13/13 4:55 PM 2/13/13 7:55 PM 2/14/13 5:5 AM 2/14/13 8:5 AM 2/14/13 11:5 AM 2/14/13 2:5 PM 2/14/13 5:5 PM 2/14/13 8:5 PM 2/15/13 5:15 AM 2/15/13 8:15 AM 2/15/13 11:15 AM 2/15/13 2:15 PM 2/15/13 5:15 PM 2/15/13 8:15 PM 2/16/13 5:25 AM 2/16/13 8:25 AM 2/16/13 11:25 AM 2/16/13 2:25 PM 2/16/13 5:25 PM 2/16/13 8:25 PM 2/17/13 5:35 AM 2/17/13 8:35 AM 2/17/13 11:35 AM 2/17/13 2:35 PM 2/17/13 5:35 PM 2/17/13 8:35 PM Date, hour Forecastng Fg.5 Compare Forecast value and Actual value n Forecast system I Power[W] Actual 2/1/13 4:25 AM 2/1/13 7:25 AM 2/1/13 1:25 AM 2/1/13 1:25 PM 2/1/13 4:25 PM 2/1/13 7:25 PM 2/11/13 4:35 AM 2/11/13 7:35 AM 2/11/13 1:35 AM 2/11/13 1:35 PM 2/11/13 4:35 PM 2/11/13 7:35 PM 2/12/13 4:45 AM 2/12/13 7:45 AM 2/12/13 1:45 AM 2/12/13 1:45 PM 2/12/13 4:45 PM 2/12/13 7:45 PM 2/13/13 4:55 AM 2/13/13 7:55 AM 2/13/13 1:55 AM 2/13/13 1:55 PM 2/13/13 4:55 PM 2/13/13 7:55 PM 2/14/13 5:5 AM 2/14/13 8:5 AM 2/14/13 11:5 AM 2/14/13 2:5 PM 2/14/13 5:5 PM 2/14/13 8:5 PM 2/15/13 5:15 AM 2/15/13 8:15 AM 2/15/13 11:15 AM 2/15/13 2:15 PM 2/15/13 5:15 PM 2/15/13 8:15 PM 2/16/13 5:25 AM 2/16/13 8:25 AM 2/16/13 11:25 AM 2/16/13 2:25 PM 2/16/13 5:25 PM 2/16/13 8:25 PM 2/17/13 5:35 AM 2/17/13 8:35 AM 2/17/13 11:35 AM 2/17/13 2:35 PM 2/17/13 5:35 PM 2/17/13 8:35 PM Date, hour Forecastng Fg.6 Compare Forecast value and Actual value n Forecast system II The data ncluded n the forecast systems are: ambent temperature, module temperature, rradance on plan 3 and rradance on plan 15. Fgure 6 shows measured and predcted values. The comparson of the results obtaned wth the two dfferent forecastng models was carred out by means of the normalzed absolute average error for the forecast method at the tme horzon of 1 h, defned as: E Max P T n ( T 1 ) * Where = generc tme nstant; n = number of observatons; P = predcted power at nstant ; T = real power at nstant. Then calculated as the mean absolute normalzed percentage error, the value s equal to 9.56% for model I and 6,53% n the second model. Ths confrms the mportance of nput data based also on weather parameters. 5. CONCLUSIONS Ths study s focused on the mplementaton of a shortterm forecastng system for the hourly electrcal energy producton n a real, grd-connected PV plant The analyzed forecast systems are based on Elman neural network. The nput varables used for the development of the models were past values of hourly energy producton n the PV plant, as well measured weather varables. A senstvty analyss has been done to verfy the mpact of the dfferent parameters to PV power generaton. In partcular multple regresson analyss has been performed to measure of how well the PV power can be predcted usng a lnear functon of a set of other varables. Results underlne the hgh mpact of rradance on PV power. Then ANN based on both measured power and meteorologcal data was revealed as the best forecastng model. Funds Ths work s supported by the Project BEAMS, Project Number , 7th Framework Program. 6. REFERENCES [1] C. Paol, C. Voyant M. Musell, M.L. Nvet, Forecastng of preprocessed daly solar radaton tme seres usng neural networks, Solar Energy no. 84, pp , 21 [2] C. Chupong and B. Plangklang Forecastng power output of PV grd connected system n Thaland wthout usng solar radaton measurement, Energy Proceda no. 9, pp , 211 [3] A. Mellt, A. Mass Pavan, A 24-h forecast of solar rradance usng artfcal neural network: ISBN:

5 applcaton for performance predcton of a grdconnected PV plant at Treste, Italy Solar Energy n.84, pp , 21 [4] M.G. De Gorg, A. Fcarella, M. Tarantno, Assessment of the benefts of numercal weather predctons n wnd power forecastng based on statstcal methods Energy no.36, pp , 211 [5] M.G. De Gorg, A. Fcarella, M. Tarantno, Error analyss of short term wnd power predcton models Appled Energy no. 88, pp , 211 [6] P.M. Congedo, M. Malvon, M. Mele, M.G. De Gorg Performance measurements of monocrystallne slcon PV modules n Southeastern Italy, Energy Converson and Management no.68, pp 1 1, 213 ISBN: