Advanced Street Lighting Control through Neural Network Ensembling

Advanced Street Lghtng Control through Neural Network Ensemblng Stefano Pzzut, Fabo Morett, Mauro Annunzato ENEA (Energy New technology and Envronment Agency) Rome, Italy stefano.pzzut@enea.t fabo.morett@enea.t mauro.annunzato@enea.t Stefano Panzer Unverstà Roma Tre DIA (Department of Informatcs and Automaton) Rome, Italy panzer@da.unroma3.t Abstract In ths work, we propose an nnovatve street lghtng energy management system n order to reduce energy consumpton. The man goal s to provde energy on demand such that energy, n ths case lght, s provded only when needed. In order to acheve ths purpose t s crtcal to have a relable demand model, whch n the case of street lghtng turns out to be a traffc flow rate forecastng model. Several methods has been compared n order to fnd out one hour predcton model. In our case studes, Artfcal Neural Networks performed best results. Moreover, several control strateges have been tested and the one whch gave the best energy savngs s the adaptve one we carred out. Expermentaton has been carred out on two dfferent case studes. In partcular we focused our expermentaton on publc street of a small and a medum szed ctes. Our studes show that wth the proposed approach t s possble to save up to 50% of energy compared to no regulaton systems Keywords-Lghtng Effcency; Energy Management Systems; Adaptve Control; Neural-Network models I. INTRODUCTION Snce the frst nternatonal recommendatons for the lghtng of roads [1], power consumpton and envronmental aspects have become more and more mportant and at the same tme, the mproved performance of lumnares and lamps, and especally the ntroducton of electronc control gears, has made t possble to ntroduce adaptve lghtng for motorzed roads and pedestrans areas. A structured model has been developed for the selecton of the approprate lghtng classes [2] (M, C, or P), based on the lumnance concept, takng nto account the dfferent parameters relevant for the gven vsual tasks. Applyng for example tme dependent varables lke traffc volume or weather condtons, the model offers the possblty to use adaptve lghtng systems wth remarkable energy consumpton savngs and therefore hgh fnancal benefts for those muncpaltes [3] where street lghtng s a hgh percentage of the electrcal bll. Today, lghtng control approaches ranges from smple on/off to regulaton systems. On/off systems nclude tmers, twlght and astronomcal clocks. The frst one s a statc system whch turns on and off street lghts always accordng to fxed tmes. The second one has lght-senstve photocells to turn them on at dusk and off at dawn [5]. The thrd ones are GPS based street lght controllers whch operate the on/off of the street lght accordng to the locaton features (longtude, lattude, sunrse, sunset tmes). Regulatons systems are based on dmmable LED or hgh pressure sodum vapor lghts [4] and allow to schedule lghts on or off and set dmmng levels of ndvdual or groups of lghts. All these systems have one common feature: they do not care about the real on-lne demand and ths s a source of hgh neffcency. Thus, n order to overcome the man lack of the current regulaton systems, t has recently started the new Intellgent Street Lghtng (ISL) approach whch looks very promsng [5]-[6]. Therefore, here we propose an ISL approach (Smart Adaptve Control) based on the concept of energy on demand, whose goal s to dynamcally set the lght ntensty as functon of the foreseen demand, namely the traffc flow rate 1 hour forecast. Thus, n such context the demand model has a crtcal role and ts accuracy strongly affects the performance of the regulaton system. In the last decade, one of the most wdely used method n order to solve modelng problems s that of Artfcal Neural Networks (ANN) [7]-[8]. In partcular, traffc flow forecastng ssue has been tackled through ANN snce the nnetes [9]-[19]. As example, among the most recent work [19] focuses on traffc flow forecastng approach based on Partcle Swarm Optmzaton (PSO) wth Wavelet Network Model (WNM). Pamula [16] revews neural networks applcatons n urban traffc management systems and presents a method of traffc flow predcton based on neural networks. Bucur et al. [17] proposes the use of a selfadaptve fuzzy neural network for traffc predcton suggestng an archtecture whch tracks probablty dstrbuton drfts due to weather condtons, season, or other factors. All the mentoned applcatons have one feature n common: they use one sngle global model n order to perform the predcton. Our approach s to use not only one model but an ensemble of models. In Secton II an overvew of modelng 76

methods used s gven, n partcular statstcal and Artfcal Neural Network based models and ther combnaton trough ensemblng. In Secton III we show results obtaned on two case studes and then n Secton IV we dscuss future works. used to model f a are the hyperbolc tangent, the sgmod and the lnear functon. II. MODELING METHODS In ths secton, we shortly descrbe the modelng and control technques we compared n the expermentaton. A. Statstcal Modelng One the smplest and most wdely used model s to buld an average weekly dstrbuton of the traffc flow rate sampled hourly. Thus, from the data we compute for each day the average traffc flow rate hour by hour n such a way that we get an average dstrbuton made of 24X7=168 ponts. B. Artfcal Neural Networks Artfcal Neural Networks (ANN) are computatonal models whch try to smulate some propertes of bologcal neural networks n order to solve complex modelng problems of non-lnear systems. An ANN s an nterconnected group of artfcal neurons (called also nodes) that uses a mathematcal or computatonal model for nformaton processng based on a connectonstc approach to computaton. In more practcal terms ANN are non-lnear data modelng or decson makng tools whch can be used to model complex relatonshps between nputs and outputs or to fnd patterns n data. ANN are referred also as blackbox or data-drven models and they are manly used when analytcal or transparent models cannot be appled. Buldng such models needs several stages as nput analyss and tranng through algorthms whch mnmze the error between the real values to be modeled and the ANN output. ANN demonstrated ther effectveness n modelng many real-world applcatons. Once we model an ANN model, we must take nto account three basc components. Frst, the synapses of the bologcal neuron are modeled as weghts. Let us remember that the synapse of the bologcal neuron s the one whch nterconnects the neural network and gves the strength of the connecton. For an artfcal neuron, the weght s a number, and represents the synapse. A negatve weght reflects an nhbtory connecton, whle postve values desgnate exctatory connectons. The followng components of the model represent the actual actvty of the neuron cell. All nputs are summed altogether and modfed by the weghts. Ths actvty s referred as a lnear combnaton. Fnally, an actvaton functon controls the ampltude of the output. Mathematcally, ths process s descrbed n Fgure 1. From ths model the actvty of the neuron can be shown to be: y=f a (w x - ) (1) where s a threshold called BIAS (Basc Input Actvaton System) whch dentfes the senstvty of the neuron to respond to the external nputs. The most common functon Fgure 1. Artfcal Neuron Model Therefore each unt performs a relatvely smple job: receve nput from neghbors or external sources and use ths to compute an output sgnal whch s propagated to other unts. Apart from ths processng, a second task s the adjustment of the weghts. The system s nherently parallel n the sense that many unts can carry out ther computatons at the same tme. Wthn neural systems t s useful to dstngush three types of unts: nput unts whch receve data from outsde the neural network, output unts whch send data out of the neural network, and hdden unts whose nput and output sgnals reman wthn the network. The way unts are connected defnes the network topology or archtecture. In the past years, many of them have been studed and the most wdely used and s the feed-forward one. In ths network structure, neurons are grouped nto layers. There are at least two layers, the nput and the output, whch gather the correspondng nput and output varables. Ths basc structure s also known as perceptron [20]. Fgure 2. Feed-forward neural network topology Moreover, n order to let the model cope wth non-lnear problems, t s possble to add one or more ntermedate layers, known as hdden layers. These models are also known as mult-layer perceptrons (MLP) [21]. The flow of data from nput to output unts s strctly n one drecton (forward). The data processng can extend over 77

multple (layers of) unts, but no feedback connectons are present, that s, connectons extendng from outputs of unts to nputs of unts n the same layer or prevous layers. C. Ensemblng The term ensemble descrbes a group of learnng machnes that work together on the same task, n the case of ANN they are traned on some data, run together and ther outputs are combned as a sngle one. The goal s obtan better predctve performance than could be obtaned from any of the consttuent models. A. Modelng Street 1 600 Street 2 800 Street 3 950 The basc dea s to set the power level of the followng hour as functon of the ANN ensemble forecast. P t+1 = f ( t+1 ) (2) where P t+1 s the power level normalzed n [0,1] to be set for the next hour, t+1 s the traffc flow rate neural forecast whch s t+1 = anne( t, t-1,, t-n ) (3) where anne s the ANN ensemble result, t- s the measured traffc flow rate at tme t-. For Street lghtng applcatons functon f n (2) can be shaped n dfferent ways, among these we appled a lnear profle although nternatonal standards [2] suggest a nonlnear one that we wll apply n future work. Fgure 3. Ensemblng In the last years several ensemblng methods have been carred out [22],[23],[24]. The frst one, also known as Basc Ensemble Method (BEM), s the smplest way to combne M neural networks as an arthmetc mean of ther outputs y. Ths method can mprove the global performance [25],[26] although t does not takes nto account that some models can be more accurate than others. Ths method has the advantage to be very easy to apply. A drect BEM extenson s the Generalsed Ensemble Method (GEM) [25],[26] n whch the outputs of the sngle models are combned n a weghted average where the weghts have to be properly set, sometmes after an expensve tunng process. Other methods are Bootstrap AGGregatING (BAGGING) [27] and Adaboost [28],[29]. III. EXPERIMENTATIONS In ths secton, we test and compare the methods presented n the prevous sectons. We used two test cases: one has concerned Tern and the second regards S.Govann n Persceto. In the frst one, we focused on three dfferent urban streets of Tern (Table I). The data set s made of 3 months (13 weeks) of measurement correspondng to 2184 hourly samples. The data set has been parttoned nto tranng/testng and valdaton made respectvely of 10 and 3 weeks each. TABLE I. STREET FEATURES Maxmum traffc flow rate If k < 0.25 then f = 0.5 If k > 0.5 then f = 1 Else f = 2 k (4) where k s the predcted traffc flow rate at tme k (3) normalzed n [0,1]. The ANN are feed-forward MLP wth 10 hdden neurons and one output (the one hour flow forecast) wth sgmod as actvaton functon for all the neurons. The number of nputs s the same of the dynamcs wndow length (3) and t has been chosen wth a prelmnary analyss by calculatng the valdaton predcton error, after the ensemblng stage, for dfferent number of hourly samples (Table II). Snce we obtaned the mnmum predcton error wth a tme wndow of eght hours, we chose the same number of nputs for the ANN, representng the length of tme hstory wndow. So each nput contans the traffc flow of one of eght hours of the tme wndow. Tranng has been performed through the Back-Propagaton algorthm wth adaptve learnng rate and momentum stoppng after 108 teratons and a save best strategy to avod overfttng. The reported results are averaged over 10 dfferent runs (wth standard devaton n brackets) and the ensemble s therefore made by the same 10 models. The reported errors are measured as: e = x-y /(M-m) (5) Where x s the real value to be predcted, y s the output model, M s the real maxmum value and m s the mnmum. 78

TABLE II. WINDOW HISTORY LENGTH (HOURS) SELECTION Number of Street 1 Street 2 Street 3 Samples 3 5.72% 6.88% 5.81% 5 3.9% 5.07% 3.99% 8 3.29% 3.43% 3.02% 10 3.54% 4.12% 3.74% At last, Table III shows the comparson of the models consdered n ths work n terms of predcton accuracy over the valdaton set. Fgure 4 shows a graphcal comparson. We compared real hourly traffc flow rate wth predcton provded from statstcal and neural network ensemble models. Ensemble models outperforms statstcal n all cases. TABLE III. MODEL COMPARISON Statstc ANN ANN Ensemblng Street 1 5.90% 3.74% 3.29% (±0.10%) Street 2 5.56% 3.48% 3.02% (±0.09%) Street 3 7.14% 4.00% 3.43% (±0.10%) Average 6.20% 3.74% (±0.10%) 3.25% From ths analyss t s clear that n general the ensemblng approach outperforms the statstcal approach provdng a remarkable mprovement n predcton accuracy. Such level of precson s very mportant when dealng wth applcatons lke traffc and lghtng control where the hgher the model accuracy s the more effectve the control system s. From ths graph t s clear that the ANN ensemble model performs much better than the statstcal model because, when out of normal condtons the ANN ensemblng takes nto account the real traffc dynamcs (3). B. Control In ths secton, we compare the results of the Statc Control (StaC) and the Smart Adaptve Control (SmAC) ntroduced n Secton III. In the expermentaton we calculated the energy savng of the two methods wth respect to the no-regulaton strategy, namely when lghts are always on at 100% of ther power for the whole nght. The lght on demand control assumes dmmable lghts (SAP or LED), on the streets we carred out ths study there were no such lamps and data about the real consumptons were not avalable, therefore the expermentaton has been carred out off-lne by calculatng the potental energy consumptons n the followng way. It has been assumed the maxmum hourly nomnal power consumpton to be one, then the followng quanttes have been calculated : C 100 = x 1, x 1 0, 1 (6) Where x 1 s the hourly power level for the th sample accordng to the no control strategy (nght power level always at 100%) and therefore C 100 s ts overall consumpton. C StaC = x 2, x 2 0, 0.5, 1 (7) Where x 2 s the hourly power level for the th sample accordng to the statc control (StaC) strategy (Fg. 4) and therefore C StaC s ts overall consumpton. C SmAC = x 3, x 3 [0, 1] (8) Where x 3 s the hourly power level for the th sample (4) accordng to the smart adaptve control (SmAC) strategy (Fg. 5) and therefore C SmAC s ts overall consumpton. These quanttes have been calculated over three months of actual traffc flow rates obtaned by on street col sensors. Thus, we computed the consumpton savng of the StaC and SmAC strateges wth respect to the no control approach n the followng way : S StaC = 1 - C StaC / C 100 (9) S SmAC = 1 - C SmAC / C 100 (10) In Table IV, we report these values for the three consdered streets. Fgure 4. Models comparson 79

TABLE IV. CONTROL STRATEGY COMPARISON: ENERGY SAVING StaC SmAC Street 1 25% 44.5% Street 2 25% 47% Street 3 25% 37.5% Average 25% 43% Actual hour: next traffc flow predcton based on prevous hour traffc flow Prevous week: forecast based on the same hour and the same week day of the prevous week Statstc model: averaged hourly profle Neural Ensemblng: neural network ensemblng based model Results show that t s possble to save on average 43% of energy, meanng that lamps wll work at 57% of ther nomnal power havng as nferor lmt 50% (4) and Fg. 5 n order to avod perods durng normal operaton wth almost no lght due to lght output drop. From these results t s clear that the SmAC approach provdes a remarkable mprovement n terms of energy savng (43% on average) n partcular on streets wth medum-low traffc flow rate. Moreover, n Fgure 5 t s shown an example of how the two strateges work, where on the Y axs we report the normalzed traffc flow rate values and the normalzed hourly power consumptons of the dfferent strateges. From Fgure 5 t s possble to see that the SmAC strategy s capable to follow the real demand (traffc flow rate) achevng the energy on demand concept. In partcular, t s nterestng to pont out that SmAC mproves not only energy effcency (orange dotted area) but also safety (yellow dashed area) because t provdes lght when actually needed. TABEL V. COMPARISON OF FORECASTING MODELS ERRORS Actual Hour. Prevous week Statstc model Neural Ensemblng Error 8.77% 7% 5.53% 4.39% Results show that also n ths case Neural Ensemble outperforms other methods. Then we compared three dfferent control descrbed above: constant, statc and adaptve. As shown n Fg. 6 constant control does not take n account the traffc flow, statc control does not overcome the varablty of traffc flow meanwhle adaptve control provde energy to lghtng spot proportonal to traffc flow. Fgure 6. Comparson of control strateges TABLE VI. CONTROL STRATEGY COMPARISON (ENERGY SAVING) Statc Control Adaptve Control -12% -34% Energy savng show n Table VI s a theoretcal evaluaton respect of constant control. Fgure 5. Control strategy comparson Tests performed on S.Govann n Persceto are based on a dataset of 123 days, sampled hourly, for a total of 2952 hours. Once agan we compared dfferent forecastng system: IV. CONCLUSIONS AND FUTURE WORKS In ths work, we proposed a new approach for adaptve street lghtng control based on the energy on demand dea. In order to acheve ths goal t s crtcal to have a relable demand model, whch n the case of street lghtng turns out to be a traffc flow rate forecastng model. Thus, we showed a modelng approach based on Artfcal Neural Networks Ensemblng n order to provde a one hour forecast of urban traffc flow rates. Expermentaton has been carred out on three dfferent classes of real streets and 80

results showed that the proposed approach clearly outperforms the statstcal methods (6% predcton error) achevng a 3% predcton error. The reason for that s that the neural ensemblng model s capable to provde more relable estmatons when out of standard condtons because t consders the real traffc dynamcs. Moreover, the proposed adaptve control strategy has been tested and compared to a tradtonal regulaton system on the same streets. Results showed that the adaptve control provdes, on average, energy savngs almost doubled (43% vs 25%). Future work wll frstly focus on dmmng profles accordng to nternatonal standards, then further modelng mprovements (usng more sophstcated ensemblng methods as well as tryng to develop hybrd models) wll be nvestgated and lastly, the economc mpact of the proposed methodology wll be carred out. Moreover further forecastng model can be taken n account n order to valdate the qualty of results obtaned. REFERENCES [1] CIE 115, Recommendatons for the Lghtng of Roads for Motor and Pedestran Traffc, 1995. [2] CIE 115, Lghtng of Roads for Motor and Pedestran Traffc, 2010. [3] Transport, Envronmental and communty servces commttee. Reducng Energy Consumpton - Street Lghtng. Report No TEC/87/08. The Hghland Councl, 2008. [4] M.S.Rea, J.D. Bullough, Y. Akash, "Several vews of metal halde and hgh pressure sodum lghtng for outdoor applcatons". Lghtng Research and Technology. p. 41(4): pp. 297 320, 2009. [5] Intellgent Street Lghtng Saves Up to 80% On Energy, ScenceDaly, July 2011. [6] New System of Intellgent Management of Street Lghtng Enables 80% Savngs n Energy, ScenceDaly, November 2011. [7] M.A. Arbb. The Handbook of Bran Theory and Neural Networks, The MIT Press, Cambrdge (MA), 1995. [8] S. Haykn. Neural Networks, A comprehensve foundaton" (2nd edton), Neural Networks, Prentce Hall, New Jersey, 1995. [9] D. Canca, J. Larrañeta, S. Lozano, L.Oneva, Traffc ntensty forecast n urban networks usng a multlayer perceptron. Jont Internatonal Meetng, Barcelona 1997. [10] M.S. Dougherty and M.Cobbett, Short term nter-urban traffc forecasts usng neural networks. Proc. Second DRIVE-II Workshop on Short-Term Traffc Forecastng, Delft, The Netherlands, pp. 65-79, 1994. [11] S.Ishak, P. Kotha, C.Alecsandru, Optmzaton of Dynamc Neural Network Performance for Short-Term Traffc Predcton. In Transportaton Research Record, 1836, pp. 45-56. 2003. [12] D. Park, L.R. Rlett, G. Han Spectral Bass Neural Networks for Real-Tme Travel Tme Forecastng, Journal of Transportaton Engneerng 125(6), 1999, pp. 515-523. [13] C. Taylor and D.Meldrum, Freeway traffc data predcton usng neural networks. Proc. 6th VNIS Conference, Seattle, WA, pp. 225-230, 1995. [14] J.W.C. Van Lnt, S.P. Hoogendoorn, H.J. Van Zuylen, Freeway Travel tme Predcton wth State-Space Neural Networks, Natonal Academes Press, Transportaton Research Record 1811, pp. 30-39,, Washngton D.C., 2003. [15] W.Zheng, L. Der-Horng, Q.Sh, Short-Term Freeway Traffc Flow Pre-dcton: Bayesan Combned Neural Network Approach, Journal of Transportaton Engneerng, 2006, pp. 114-121. [16] T. Pamula, Road traffc parameters predcton n urban traffc management systems usng neural networks, Transport Problems, Volume 6 Issue 3, 2011, pp. 123-128. [17] L. Bucur, A.Florea, B.S.Petrescu, An adaptve fuzzy neural network for traffc predcton, Control & Automaton (MED), 18th Medterranean Conference, Marrakech, pp. 1092 1096, 2011. [18] B.G. Çetner, M.Sar, Q.Borat, A neural network based traffc flow predcton model, Mathematcal and Computatonal Applcatons, Vol. 15, No. 2, 2010, pp. 269-278. [19] S.Jawanjal, P. Bajaj, A desgn approach to traffc flow forecastng wth soft computng tools, Emergng Trends n Engneerng and Technology (ICETET), 3rd Internatonal Conference, pp. 81-84, Goa, 2010. [20] F. Rosenblatt, The perceptron: a percevng and recognzng automaton. Report 85-460-1, Cornell Aeronautcal Laboratory, 1957. [21] F. Rosenblatt, Prncples of neurodynamcs: perceptrons and the theory of bran mechansms. Spartan Books, Washngton D.C.,1961. [22] A. Krogh, J. Vedelsby, Neural network ensembles, cross valdaton and actve learnng, Advances n Neural Informaton Processng Systems, 7, pp. 231-238, MIT Press, 1995. [23] Y. Lu, X. Yao, Ensemble learnng va negatve correlaton, Neural Networks, 12(10), 1999, pp. 1399-1404. [24] L. Breman, Combnng Predctors, Combnng Artfcal Neural Nets - Ensemble and Modular Mult-net Systems, Sprnger, Berln, pp. 31-50, 1999. [25] M.P. Perrone and L.N. Cooper, When networks dsagree: ensemble methods for hybrd neural networks. Neural Networks for Speech and Image Processng, Chapman-Hall, 1993. [26] C.M. Bshop, Neural networks for pattern recognton. Oxford Unversty Press, pp. 364-369, 1995. [27] R. Kohav and E. Bauer, An emprcal comparson of votng classfcaton algorthms: Baggng, boostng and varants, Machne Learnng, 36, 1999, pp. 105-142. [28] H. Drucker, Improvng regressors usng boostng technques, Machne Learnng, Proceedngs of the Fourteenth Internatonal Conference, pp. 107-115, 1997. [29] R. Avnmelech and N. Intrator, Boostng regresson estmators. Neural Computaton, 11, 1999, pp. 491-513. 81