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Economic Impacts of Advanced Weather Forecastingon Energy System OperationsVictor M. Zavala, Emil M. Constantinescu, and Mihai Anitescu

Abstract—We analyze the impacts of adopting advanced weatherforecasting systems at different levels of the decision-makinghierarchy of the power grid. Using case studies, we show thatstate-of-the-art numerical weather prediction (NWP) models canprovide high-precision forecasts and uncertainty information thatcan significantly enhance the performance of planning, scheduling,energy management, and feedback control systems. In addition,we assess the forecasting capabilities of the Weather Research andForecast (WRF) model in several application domains.

Index Terms—weather forecasting, operations, economics, pho-tovoltaics, wind, storage, unit commitment, energy dispatch.

I. INTRODUCTIONWeather conditions strongly influence the energy consump-

tion and performance of industrial and residential facilities. Oneof the reasons is that energy is used mostly for conditioninglarge and exposed spaces (e.g., buildings and warehouses)and for generating industrial-scale utilities (e.g., steam andcooling water) that rely on combustion and natural convectionphenomena in the presence of atmospheric air. In addition, thenext-generation energy portfolio relies on the exploitation ofintermittent resources such as solar radiation and wind power.

The operational hierarchy of energy systems involves severallayers, including planning and scheduling (or commitment),set-point optimization (or energy management), and feedbackcontrol. The weather acts as a dynamic disturbance to all theseoperational layers. Major questions arise in this context: Whatis the economic value of forecasting the weather conditions?How can this information be exploited efficiently?How long andprecise should the forecast be? Intuitively, one would expect thatanticipating weather trends allows the operational layers to reactmore proactively and thus enhances responsiveness and robust-ness. Forecast trends can be exploited systematically throughoptimization formulations of the different operational layers.In certain domains where the economic impact of the forecastaccuracy is strong, the formulation might also need to incorpo-rate uncertainty information in order to guarantee appropriateperformance and robustness. An important industrial exampleis unit commitment/energy dispatch, where ambient temperatureforecasts are used to infer the load. In this operational layer, itis desirable that the spread of the forecast error distribution beas narrow as possible in order to minimize reserves, since thesetranslate into extra costs. In some other domains, however, theeconomic value of using weather forecasting is entirely systemand task dependent and is difficult to assess.

Weather forecasting can be performed by using empiricalmodels (e.g., autoregressive, neural networks, persistence) or

E. M. Constantinescu, V. M. Zavala and Mihai Anitescu are with theMathematics and Computer Science Division, Argonne National Laboratory,Argonne, IL, 60439 USA. E-mail: {emconsta, vzavala, anitescu}@mcs.anl.gov.Preprint ANL/MCS-P1687-1009.

advanced numerical weather prediction models (e.g., physics-based). A common problem with empirical models is thattheir predictive capabilities rely strongly on the presence ofpersistent trends. In addition, they neglect the presence ofcoupled, spatiotemporal physical phenomena. Thus, they canlead to inaccurate medium and long-term forecasts and over- orunderestimated uncertainty levels [9], [5]. The use of physics-based numerical weather prediction (NWP) models is thus de-sirable because longer-term forecasts and narrower uncertaintybounds can be obtained. On the other hand, from an operationalpoint of view, the practical capabilities of NWP models arelimited. One of the major limiting factors is their computationalcomplexity. For instance, performing data assimilation everyhour at a high spatial resolution is currently not practical. Inaddition, extracting uncertainty information from NWP modelsquickly becomes intractable from the point of view of bothsimulation time and memory requirements. This is an importantissue because NWP models are expected to be used to makelow- and high-frequency operational decisions (time scales fromdays to minutes).

In this work, we present an integrative study of weatherforecasting, uncertainty quantification, and optimization-basedoperations. We first analyze the impact of increasing the forecasthorizon on energy management operations for a multi storagehybrid system and on the temperature control of a buildingsystem. With this information, we seek to understand underwhich conditions weather forecasts are beneficial. We thencompare the forecasting capabilities of an empirical modelingapproach with those of the Weather Research and Forecast(WRF) model. Here, we seek to demonstrate that WRF canprovide accurate and consistent uncertainty bounds for differentweather variables in reasonable computational time. However,limitations do exist in some domains. Therefore, we discussimplementation bottlenecks and sources of error excursions, andwe propose potential enhancements.

II. ECONOMIC IMPACT OF FORECASTING

In this section, we analyze two systems to illustrate theeconomic impact of folding weather forecasts in operations.

A. Multi-Storage Hybrid System

We first consider a photovoltaic system coupled to two storageoptions described in [14], [16] and sketched in Fig. 1. The firststorage option has a large capacity but low round-trip efficiency(hydrogen with 70% efficiency), while the second has a smallcapacity but high efficiency (battery with 90% efficiency). Theoperating principle of this system is similar to that of other multistorage systems, such as photovoltaic-compressed air-battery ,orwind-hydrogen-hydrothermal systems [10]. In these systems, it

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is necessary to decide the best strategy to store the intermittentpower input in order to minimize power losses and satisfy agiven load. To compute the optimal policies, we formulate anoptimal control problem with forecast horizons ranging from1 hour to 14 days [17]. In Fig. 2, we present the effect ofincreasing the horizon on the relative operating costs (using aone-year forecast policy as reference). Several conclusions canbe drawn from this study: (1) the relative operating costs decayquickly to zero as the horizon is increased; (2) for a purelyreactive strategy (1 hr), the relative costs can go as high as300%; and (3) the close-to-optimal costs can be obtained witha relatively short forecasts (1-14 days). The economic penaltyof using a forecast of 1 day is just an increase of 10% in relativecosts, whereas the penalty for a forecast of 12 hr goes up to 31%.This implies that a practical forecast horizon should capture theperiodicity of the daily radiation. For this system, as the horizonis increased, it is possible to exploit the more efficient batterysystem to reduce the power losses of the hydrogen storage loop.The magnitude of the cost reductions is problem dependent,but the behavior is expected to be consistent in other storagesystems. The major factors affecting the impact of the forecastare the relative efficiency and size of the storage devices.

=

=

=

=

=

=

Fuel CellElectrolyzer Battery Bank

DC-DC

Converters

Distribution

Busbar

=

=

DC-AC

Inverters

Solar

Radiation

Hydrogen Storage

Load

=

=

Grid

Fig. 1. Schematic representation of multi storage hybrid system.

1 hr 4 hr 6 hr 12 hr 1 day 3 day 7 day 14 day0

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e C

ost [

%]

Horizon [Days]

Fig. 2. Effect of forecast horizon on operational costs of multi storage hybridsystem.

B. Building System

Commercial buildings are energy-intensive facilities whereconsiderable cost savings can be realized through optimaltemperature control strategies. Researchers have found that the

Ambient

Temperature

Wall

HVAC

System

Fig. 3. Schematic representation of building system.

1 hr 3 hr 6 hr 9 hr 12 hr 16 hr 24 hr0

5

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Horizon

Rel

ativ

e C

ost [

%]

Fig. 4. Effect of forecast horizon on operational costs of building system.

thermal mass of a building can be used for temporal energystorage [2]. With this, it is possible to optimize the temperatureset-points trajectories during the day to shift the heating andcooling electricity demands to off-peak hours and thus reducecosts [3]. Since the thermal response of the building can be slow(order of hours), this can be exploited to reduce the on-peakelectricity demand the next day. However, the optimal timingat which to start the cooling at night directly depends on theambient temperature expected the next day. In addition, specialcare needs to be taken to stay within the thermal comfort zoneat all times. To analyze the effect of increasing the forecasthorizon of the ambient temperature, we formulate an optimalcontrol problem of the general form presented in Section IV.The problem involves considering the dynamic response ofthe building internal temperature and of the building wall andtrying to find the optimal temperature set-point that minimizesthe heating and cooling costs under a given electricity pricestructure. The ambient temperature enters the model through aboundary condition at the external face of the wall [15].

The relative costs are presented in Fig. 4. As can be seen,for a purely reactive strategy, the relative costs can go as highas 24%. In addition, we observe that a horizon of 1 day issufficient to achieve the minimum potential costs. The reasonis that the thermal mass of the building cannot be used for along time because there exist losses through the wall. In fact, wefound that if the building insulation is enhanced, the costs for thepurely reactive strategy increase significantly. On the other hand,when the building is poorly insulated, increasing the forecasthorizon does not reduce the costs. In other words, the economicpotential of adding forecast information is tightly related to theability to store energy in the system and to use it during off-peak

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times. Another potential benefit of using forecast information isto minimize the number of start ups and shut downs of waterchillers and gas furnaces, thereby reducing electricity and fuelcosts and enhancing the responsiveness of the HVAC system.

III. WEATHER FORECASTING

In this section, we contrast the forecasting capabilities ofempirical and physics-based weather models.

A. Gaussian Process Model

A straightforward forecasting alternative is to use historicalmeasurement data to construct regression models. Several em-pirical modeling techniques can be used to generate weatherforecast trends. An approach that has recently received attentionis Gaussian process (GP) modeling [11], [12], [7], [8]. Theidea is to construct an autoregressive model by specifying thestructure of the covariance matrix rather than the structure ofthe dynamic model itself, as in traditional system identificationtechniques such as the Box-Jenkins approach [1]. To illustratethe use of this technique, we construct a forecast model byregressing the future weather variable value (output) χk+1 to thecurrent and previous values (inputs) χk, ..., χk−N that can beobtained from weather information data bases. In this case, N isselected long enough to capture the trends of the variable of in-terest. We define the model inputs as X[j] = [χk−N−j , ..., χk−j ]and the outputs as Y[j] = χk+1−j , and we collect a number oftraining sets j = 0, ..., Ntrain. We assume that the inputs arecorrelated through an exponential covariance function of theform

V(X[j],X[i], η) := η0 + η1 · exp(− 1η2∥X[j] −X[i]∥2

),

i = 0, ..., Ntrain, j = 0, ..., Ntrain,

where η1, η2, and η3 are hyperparameters estimated by maxi-mizing the log likelihood function

log p(Y|η) = −12YTV−1(X,X, η)Y − 1

2log det(V(X,X, η)).

Once the optimal hyperparameters η∗ are obtained, we cancompute mean predictions YP with associated covariance VP

at a set of test points XP . In our context, these are the evolvingweather trends. The resulting GP posterior distribution is

YP = V(XP ,X, η∗)V−1(X,X, η∗)Y

VP = V(XP ,XP , η∗)

−V(XP ,X, η∗)V−1(X,X, η∗)V(X,XP , η∗).

The inverse of the input covariance VX := V−1(X,X, η∗)(e.g., its factorization) needs to be computed only during thetraining phase. With this, we can define a conceptual GP modelof the form

YP = GP(XP , η∗,VX).

Note that at current time tk, we have measurements to computeonly the single-step forecast χ̄k+1. To obtain multi step fore-casts, we must propagate the GP predictions recursively. We usethe following algorithm,

1) Forecast mean computation: For j = 1, ..., NF do,a) Set XP[j] ← [χk−N , χk−N+1..., χk]

b) Compute YP[j] = GP(XP[j], η

∗,VX)

c) Drop last measurement, set χk+1 ← YP[j], andupdate k ← k + 1

2) Forecast covariance computation: Compute self-covariance V(XP ,XP , η∗) and cross-covarianceV(XP ,X, η∗). Compute forecast covariance VP from(1a).

This recursion generates the forecast mean YP =[χ̄k+1, ...., χ̄k+NF ] and associated covariance matrix V

P .The resulting distribution N (YP ,VP ) can be sampled togenerate realizations of the future weather trends. Note that theforecasted trends are local (single point in space) and thus donot capture effects at neighboring locations.

B. WRF Model

We now describe the procedures used to compute forecastsusing WRF. The WRF model [13] is a state-of-the-art NWPsystem designed to serve both operational forecasting andatmospheric research needs. WRF is the result of a multi-agency and university effort to build a highly parallelizablecode that can run across scales ranging from large-eddy toglobal simulations. WRF has a comprehensive description of theatmospheric physics that includes cloud parameterization, land-surface models, atmosphere-ocean coupling, and broad radiationmodels. The terrain resolution can go up to 30 seconds of adegree (less than 1 km2). To initialize the NWP simulations,we use reanalyzed fields. In particular, we use the NorthAmerican Regional Reanalysis (NARR) data set that coversthe North American continent (160W-20W; 10N-80N) with aresolution of 10 minutes of a degree, 29 pressure levels (1000-100 hPa, excluding the surface) every three hours from 1979 tothe present. We use an ensemble of realizations to representuncertainty in the initial (random) wind field and propagateit through the WRF nonlinear model. The initial ensemble isobtained by sampling from an empirical distribution. A similarapproach is presented in [15].

1) Ensemble Initialization: The NWP system evolves a givenstate from an initial time t0 to a final time tF . The initial stateis produced from past simulations and reanalysis fields, that is,simulated atmospheric states reconciled with observations in thedata assimilation step. Because of observation sparseness in theatmospheric field and the incomplete numerical representationof its dynamics, the initial states are not known exactly and canbe represented only statistically. We use a normal distributionof the initial conditions to describe the confidence in theknowledge of the initial state of the atmosphere. The distributionis centered on the NARR field at the initial time, the mostaccurate information available. The covariance matrix V isapproximated by the sample variance or pointwise uncertaintyand its correlation, C. The initial NS-member ensemble fieldxt0s := xs(t0), i ∈ {1 . . . NS}, is sampled from N (xNARR,V):

xt0s = xNARR +V12 ξs , ξs ∼ N (0, I) , s ∈ {1 . . . NS} , (2)

where C = Vij/√

ViiVjj and Vii is the variance of variable i.This is equivalent to perturbing the NARR field with N (0,V).That is, xs = xNARR +N (0,V). In what follows, we describethe procedure used to estimate the correlation matrix.

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2) Estimation of the Correlation Matrix: In weather models,the correlation structure is typically localized in space. There-fore, in creating the initial ensemble, one needs to estimate thespatial scales associated with each variable. To obtain thesescales, we build correlation matrices of the forecast errorsusing the WRF model. These forecast errors are estimatedby using the NCEP method [6], which is based on startingseveral simulations staggered in time in such a way that, atany time, two forecasts are available. The differences betweentwo staggered simulations is denoted as dij ∈ RN×(2×30days),that is, the difference at the ith point in space between thejth pair of forecasts, where N is the number of points in spacemultiplied by the number of variables of interest. The covarianceand correlation matrices can be approximated by V ≈ ddT.

3) Ensemble Propagation through the WRF Model: Theinitial state distribution is evolved through the NWP modeldynamics. The resulting trajectories can then be assembled toobtain an approximation of the forecast covariance matrix:

xtFs =Mt0→tF(xt0s

)+ ηs(t) , s ∈ {1 . . . NS} , (3)

where xt0s ∼ N (xNARR,Vt0), ηs ∼ N (0,Q), and Mt0→tF (•)represents the evolution of the initial condition through theWRF model from time t0 to time tF . The initial condition isperturbed by the additive noise η that accounts for the variouserror sources during the model evolution. An analysis of thecovariance propagation through the model is given in [15].

−80 −60 −40 −20 0 20 40 60 80 100 12010

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Time Step [−]

Tem

pera

ture

[oC

]

← Past → ← Future →

Fig. 5. Ambient temperature observations and GP model realizations for afive-day forecast horizon.

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Tem

pera

ture

[oC

]

Fig. 6. Ambient temperature observations and WRF model realizations for afive-day forecast horizon.

C. Model Validation

We next validate the forecast information obtained from theWRF and the GP models. An ambient temperature data set

at position 40 30’N/80 13’W in the Pittsburgh, PA, area foryear 2006 was used. The data were obtained from the NationalWeather Service Office. To assess the forecasting capabilities ofthe GP model, we used a total of 120 training sets and we setN to 24. We consider a forecast horizon of 5 days. In Fig. 5,we present the forecast mean and 100 realizations drawn fromthe predictive distribution. We can see that the GP model isable to capture the periodicity of the trends. However, the meandrifts away from the true realizations and the uncertainty boundsare not able to encapsulate the actual realizations. Since thedistribution is inconsistent, it is not useful from an operationalpoint of view. Note that the temperature follows variations asa result of spatial interactions and long-term metereologicalphenomena. This situation might explain the lack of strongerperiodic trends. This might also explain the poor quality of theforecasts. Nevertheless, we emphasize that empirical models arevaluable for high-frequency operations (on the order of secondsor minutes). We now validate the forecast and uncertainty

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Fig. 7. Wind speed realizations for 6 wind-farm locations in Illinois at 10 m andobservations (dots) at nearest meteorological stations. Vertical lines representbeginning of day.

information of the WRF model. In Fig. 6, we show the predictivedistribution for the temperature constructed using 30 ensemblemembers and a forecast horizon of 5 days. Note that the forecasterrors are small (±5oC) and the uncertainty bounds enclose thetrue observations tightly. We also present validation results ofwind speed forecasts generated by WRF at 6 active wind-farmsin the state of Illinois to analyze their accuracy and correlationstructure. The wind speed fields at 10 meters above the groundfor three consecutive days are presented in Fig. 7. We note thatthe WRF realizations are able to capture the general trends of theactual observations and to encapsulate the observations with few

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° Longitude W

° La

titud

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Fig. 8. The spatial correlation for the wind field for wind farm #8 on June 5,1:50 AM, denoted by “X.” The circle markers denote the other wind farms inIllinois.

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exceptions. The largest differences are observed at the beginningof the third day. The wind speed trends are clearly more difficultto predict than temperature trends. In Fig. 8 we show the spatialcorrelations of the wind speed for a particular wind farm asinferred from the 30-member WRF ensemble simulation. Thewind speed is highly correlated over the studied region, and ithas a nontrivial spatial structure. This observation is confirmedby comparing Figs. 7 and 8. Here, we can see that the windspeed realizations for wind farms #2, 3, and 4 are stronglycorrelated, as predicted by the correlation mapping.

In this study we used version 3.1 of WRF [13]. The ensembleapproach taken for estimating the uncertainty in the weathersystem is highly parallelizable because each realization evolvesindependently through WRF. The most expensive computationalelement is the evolution of each sample through the WRF sys-tem. We therefore consider a two-level parallel implementationscheme. The first level is a coarse-grained task decompositionrepresented by each sample. A secondary, finer-grain levelconsists in the parallelization of each sample. Our runningtimes indicate that around 32 CPUs are sufficient to generateupdated forecasts with WRF for intra day operations (e.g.,unit commitment). The times also suggest that, in order togenerate updated forecasts for hourly operations (e.g., energymanagement), one would need about 500 CPUs.

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Weather Model Based

Fig. 9. Operating policies with different forecasts. Thermal comfort zone ishighlighted by thick solid lines, predicted temperatures are gray lines, and actualrealizations are dashed lines.

IV. INTEGRATION OF FORECASTING AND OPERATIONS

In this section we discuss how forecast accuracy and un-certainty information affect the performance of different opera-tional layers.

A. Building Temperature Control

As discussed in Section II, optimal set-points for internalbuilding temperatures can be obtained by solving an optimalcontrol problem. We can extend this formulation to considermultiple future realizations of the ambient temperature. The

problem can be cast as a sample-average stochastic optimalcontrol problem:

minu(τ)

1

Ns

Ns∑s=1

[∫ tk+Ttk

φ(zs(τ), ys(τ), u(τ), χs(τ))dτ

](4a)

s.t.dzsdτ

= f (zs(τ), ys(τ), u(τ), χs(τ))

0 = g(zs(τ), ys(τ), u(τ), χs(τ))0 ≥ h(zs(τ), ys(τ), u(τ), χs(τ))

τ ∈ [tk, tk + T ](4b)

zs(tk) = xk, s ∈ S. (4c)

The objective function is the future average cost; the constraintsinclude a differential-algebraic equation system describing thebuilding dynamics and a set of operational constraints. Thedynamic model is defined over each possible realization ofthe weather conditions, which are denoted by χs(τ), s ∈ S,and presented in Figs. 5 and 6. We solve this problem usingrealizations obtained from the GP and WRF model distributions.The optimal temperature set-point profiles are presented in Fig.9. In the top graph, we present the profile for the ideal strategywhere perfect forecast information is assumed. Since there is nouncertainty, the predicted profile matches the actual realization.Note that the optimal profile hits continuously the bounds ofthe comfort zone, as it tries to take advantage of the on-peakand off-peak electricity rates to minimize costs. In the middlegraph, we present the optimal profiles obtained using forecastinformation from the GP model. The gray lines are the predictedrealizations of the dynamic model in the stochastic optimalcontrol formulation. Since the uncertainty structure provided byGP is not able to capture the ambient temperature observations,the actual realization of the internal building temperature goesoutside the comfort zone. In the bottom graph, we see that theuse of the WRF model realizations results in an temperaturetrajectory that stays within the comfort zone at all times. Notethat, since the comfort zone is very narrow (≈5oC), high-precision forecast information is needed to realize the economicbenefits.

B. Unit Commitment and Energy Dispatch

We now analyze the accuracy of wind speed forecasts ob-tained with WRF in unit commitment/energy dispatch opera-tions with large adoption levels of wind power. The systemspecifications are provided in [4]. The sample-average stochasticformulation is

minps,j,k,ps,j,k,νj,k

1

NS

∑s∈S

∑j∈N

∑k∈T

cps,j,k + cuj,k + c

dj,k

(5a)s.t.

∑j∈N

ps,j,k +∑

j∈Nwind

pwinds,j,k = Dk, s ∈ S, k ∈ T (5b)∑j∈N

ps,j,k +∑

j∈Nwind

pwinds,j,k ≥ Dk +Rk, s ∈ S, k ∈ T

(5c)ps,j,k ∈ Πs,j,k, s ∈ S, j ∈ N , k ∈ T . (5d)

Here, ps,j,k are the power outputs of the thermal units, pwinds,j,kare the wind power outputs of a set of turbines, Dk is thedemand, Rk are the reserves, and Πs,j,k is a set of feasible

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outputs that implicitly takes into account ramp, start-up, andshutdown constraints. In Fig. 10, we present the profiles oftotal aggregated demand, thermal power, and wind power forthree days of operation. The wind power profiles were generatedby using the wind speed realizations presented in Fig. 7. Notethat the aggregated wind power profile does not follow a strongperiodic trend. Nevertheless, the WRF realizations are able toencapsulate the actual profiles (solid lines) during the first twodays. As a result, the optimizer is always able to satisfy the load,even for an adoption level of 20%. In the third day, however,we see a significant mismatch between the forecasted windpower and the realized one in the first 12 hours of operation.In this case, the reserves are sufficient to satisfy the load.However, this result suggests that a high frequency and adaptiveinflation/resampling procedure is needed in WRF. This clearlypoints out the need for more application-oriented forecastingcapabilities. Moreover, we found that a deterministic strategy(using only the WRF forecast mean) is not able to sustainadoption levels of more than 10% even with the allocatedreserves.

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200

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600

800

1000

1200

Tot

al P

ower

[MW

]

Time [hr]

Fig. 10. Closed-loop total power profiles obtained with stochastic UC formu-lation. Top thick line is demand profile, medium thick line is the implementedthermal profile, gray lines are planned realizations at beginning of each day,bottom thick line is actual total wind power, and the adjacent gray lines areforecast profiles.

V. CONCLUSIONS

We have presented an integrative study of weather forecast-ing, uncertainty quantification, and optimization-based opera-tions to analyze the economic impacts of adopting advancedweather forecasting systems at different operational levels ofthe power grid. Our studies suggest that costs and power lossescan be reduced by incorporating accurate forecasts. In addi-tion, our model validation studies indicate that WRF forecastssignificantly outperform those obtained with empirical models,specially for medium- and long-term horizons. As future work,we are interested in developing application-oriented forecasts tominimize computational limitations. In addition, targeted dataassimilation and adaptive inflation resampling procedures arenecessary to correct forecasts at higher frequencies.

ACKNOWLEDGMENTS

This work was supported by the Department of Energy, underContract No. DE-AC02-06CH11357.

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Emil M. Constantinescu received his B.Sc. and M.S. degrees from the Collegeof Automatic Controls and Computers, Bucharest Polytechnic University, Ro-mania (2001,2002) and the Ph.D. degree from Virginia Tech (2008) in computerscience. He is currently the Wilkinson Postdoctoral Fellow in the Mathematicsand Computer Science Division at Argonne National Laboratory. His researchinterests include uncertainty quantification in weather and climate models andtheir applications to energy systems.

Victor M. Zavala is currently a Director’s Postdoctoral Fellow in the Mathemat-ics and Computer Science Division at Argonne National Laboratory. He receivedthe B.Sc. degree from Universidad Iberoamericana (2003) and the Ph.D. degreefrom Carnegie Mellon University (2008), both in Chemical Engineering. He hasserved as a research intern at ExxonMobil Chemical Company (2006, 2007) andas a consultant for General Electric Company (2008). His research interests arein the areas of mathematical modeling and optimization of energy systems.

Mihai Anitescu has obtained his Engineer (M.Sc.) Diploma in electricalengineering from the Polytechnic University of Bucharest (1992) and his Ph.D.degree in applied mathematical and computational sciences from the Universityof Iowa (1997). He is currently a Computational Mathematician in the Mathe-matics and Computer Science Division at Argonne National Laboratory and aProfessor in the Department of Statistics at the University of Chicago. He is theauthor of more than 60 papers in scholarly journals and conference proceedings,on numerical optimization, numerical analysis, computational mathematics andtheir applications.

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