One of the key problems in renewable energy systems is how to model and forecast the energy flow. The paper first investigates various stochastic times-series models to predict energy production and consumption, then suggests an online learning method which adaptively aggregates the different forecasts while also taking side information into account. The approach is demonstrated on data coming from a prototype public lighting microgrid which includes photovoltaic panels and LED luminaries

Csáji, Balázs Csanád

Kovács, András

Váncza, József

SZTAKI Publication Repository

ERCIM NEWS 107   October 201640Special theme: Machine LearningThe presented research was motivatedby the E+grid project which aims atbuilding an energy-positive publiclighting microgrid using photovoltaicpanels, LED luminaries that regulatetheir lighting levels based on motionsensor signals, energy storage, varioussensors and smart meters, wireless com-mination and a central controller (seeFigure 1).  A prototype system wasdeveloped by an industry-academy con-sortium formed by GE Hungary, theBudapest University of Technology andEconomics, and two institutes of theHungarian Academy of Sciences (MFAand SZTAKI). The physical prototype,containing 191 luminaries and 152 m2of PV panels, is located in Budapest atthe campus of the MFA Institute of theHungarian Academy of Sciences [1]. Stochastic Models of Energy FlowA crucial problem in renewable energysystems is to model energy flow. It is achallenging task, as both energy produc-tion and energy consumption areaffected by various external factors, andhence, highly uncertain and dynami-cally changing. On the other hand, suchmodels are needed to generate forecastsand to build efficient controllers.Several models have been suggested forthis in the past, including clear-skymodels (i.e., an estimate of the terres-trial solar irradiance under the assump-tion of a cloudless sky based on astro-nomic calculations), persistenceapproaches, autoregressive models,neural networks, fuzzy and hybridmodels [2]. During the E+grid project we experi-mented with a number of time-seriesmodels and, after suitable preprocessing(such as removing outliers, noise reduc-tion and normalisation), fitted separatedynamic models to the energy produc-tion and consumption processes. Weused discrete-time stochastic modelswith one hour as the time step. Theapplied models can be classified in twogroups: linear and nonlinear. The linearmodels were: FIR (finite impulseresponse), AR (autoregressive), ARX(autoregressive exogenous), ARMA(autoregressive moving average), BJ(Box-Jenkins) and state space, while thenonlinear models were: HW(Hammerstein-Wiener), Wavelet, MLP(Multilayer Perceptron), MLPX (MLPwith exogenous inputs), SVR (SupportVector Regression) and SVRX (SVRwith exogenous inputs) [2]. For the models with exogenous compo-nents, we supplied side information asthe inputs to help, for example, to copewith the quasi-periodic nature of theproblem as well as to provide the avail-able background knowledge on themodelled phenomenon. Side informa-tion included the clear-sky predictionfor the case of photovoltaic energy pro-duction, while it was the typical con-sumption pattern (based on historicaldata) for the specific hour of the day, incase of consumption.After the models were identified, theinnovation (noise) sequences driving theprocesses were estimated. Based on theOnline Learning for Aggregating forecasts in Renewable Energy Systemsby Balázs Csanád Csáji, András Kovács and József Váncza (MTA SZTAKI)One of the key problems in renewable energy systems is how to model and forecast the energy flow. AtMTA SZTAKI we investigated various stochastic times-series models to predict energy production andconsumption, and suggested an online learning method which can adaptively aggregate differentforecasts while also taking side information into account. The approach was demonstrated on datacoming from a prototype public lighting microgrid containing photovoltaic panels and LED luminaries.Figure1:ArchitectureoftheE+gridlightingsystem.ERCIM NEWS 107   October 2016 41process and the noise models, forecastswere made by Monte Carlo methods. Inthe E+grid system, 24-hour forecastswere generated hourly and were used bya receding horizon controller [1].Online Learning for ContextDependent Forecast AggregationWhile experimenting with various sto-chastic models we observed that therewas no uniformly best model; somemodels performed better in some situa-tions but worse in others. Since gener-ating forecasts with the already esti-mated models is computationally cheap,we decided to use all of the models andaggregate their predictions online,based on their past performances insimilar situations.For online learning the best forecasts,we applied the framework of predictionwith expert advice. In this framework alearner sequentially faces the problemof predicting an unknown environmentbased on the predictions and past per-formances of a pool of experts. Thelearner aims at minimising its regret,i.e., the difference of its cumulative losscompared to that of the best performingexpert so far. The loss is typicallydefined as the distance between the pre-dicted and actual outcomes of particularvariables in the environment. A stan-dard and widely used aggregation ruleto combine the predictions of theexperts based on their past losses is theexponentially weighted average fore-caster (EWAF) [3].In our case the experts were the esti-mated time-series models based onwhich the forecasts were generated. Werefined the standard framework bytaking contextual information intoaccount as well; namely the losses wereweighted by a suitably defined simi-larity kernel which described how sim-ilar the current situation was to the pastone in which the expert (the stochasticmodel) incurred the loss. We alsoapplied discounting to help focus onrecent events (e.g., losses incurred along time ago had lower weights). Inaddition to the similarity and temporalweighting, our approach, called thestate dependent average forecaster(SDAF) was similar to EWAF, e.g.,exponential weighting was applied [2].Experimental Results andConclusionsSeveral numerical experiments wereperformed on the energy production andconsumption data coming from the pro-totype E+grid system [2]. Our resultsindicate that the applied time-seriesmodels, especially the ones using sideinformation, can be efficiently appliedto forecast the energy flow in thesystem. They also demonstrate (seeFigure 2) that aggregated approachescan provide better forecasts than singletime-series models in themselves.Furthermore, they show that our contextdependent aggregation approach(SDAF) outperforms the standard con-text independent EWAF for this kind ofprediction problems.This work has been supported by theHungarian Scientific Research Fund(OTKA), projects 113038 and 111797.B. Cs. Csáji acknowledges the supportof the János Bolyai ResearchFellowship No. BO/00217/16/6.References:[1] A. Kovács, R. Bátai, B. Cs. Csáji,P. Dudás, B. Háy, G. Pedone, T.Révész, and J. Váncza: “IntelligentControl for Energy-Positive StreetLighting”, Energy, Elsevier, Vol. 114,2016, pp. 40–51.[2] B. Cs. Csáji, A. Kovács, and J.Váncza: “Adaptive AggregatedPredictions for Renewable EnergySystems”, in Proceedings of the 2014IEEE Symposium on AdaptiveDynamic Programming andReinforcement Learning (ADPRL2014), Orlando, Florida, Dec. 9-12,2014, pp. 132-139.[3] N. Cesa-Bianchi, G. Lugosi:“Prediction, Learning, and Games”,Cambridge University Press, 2006.Please contact:Balázs Csanád CsájiMTA SZTAKI, Hungary+ 36 1 279 6231balazs.csaji@sztaki.mta.hu310 315 320 325 330 335 340 345 350 355 36000,050,10,150,20,250,30,350,4timediscounted loss  bestavgewafsdafFigure2.Discountedcumulativelossofpredictingphotovoltaicenergyproductionforthebest,theaverage,theexponentiallyweightedaverageandthestate-dependentexponentiallyweightedaverageforecaster.

Online Learning for Aggregating Forecasts in Renewable Energy Systems

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Online Learning for Aggregating Forecasts in Renewable Energy Systems

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