Predicting Energy Consumption using Panel Data Analysis

1. Predicting energy consumption usingpanel data analysisAdela Has, Ana Bilandžić, Marijana Zekić-Sušac presented at the 17th International Conference onOperational Research KOI 2018 September 26 – 28, 2018 Zadar, Croatia

2. Outlineofthepresentation • Introduction • Review of previous research • Methodology • Results • Conclusionandlimitations • References

3. Introduction • Everyeconomy that thrives for sustainable development faces the challenge of reducing energy consumption • The building sector is considered as the biggest single contributor to world energy consumption and greenhouse gas emissions. • Building energy use currently accounts for over 40% of total primary energy consumption in the US and EU

4. Whatwe are dealinghereandwhyitisimportant? The aim of this paper is to develop a model for predicting energy consumption of public buildingsin Croatiabased on panel analysis. • Several institutions in Croatia were founded by the government in order to record and measure energy consumption and efficiency of buildings (APN, CEI) • almost70% of buildings in the Croatia were built before 1980 and don’tmeet a existing energy standards (Krstić & Teni, 2017), • for many public buildings in Croatia, the energy consumption level is missing which aggravates the resource allocation for reconstruction measures, • lack of using intelligent data analysis in public buildings energy efficiency, • no real time monotoringofenergyconsumptionsinpublicbuildings.

5. Review of previous research • Energy consumption and management is a frequenttopic in recent research. • A numerus ofresearch have been conducted inorder to predictenergyconsumption, to explorebehavioursofbuildingsand to identifythe most significantcharacteristicsofbulidings. • Foucquieret. al (2013) distinguishthreeapproachesofbuilding performance evaluation methodologies: • white box - based on physical models, • black box - machine learning models used for prediction of energy consumption, heating/cooling demand, indoor temperatureand • grey box – modelswhichuse both physical and statistical techniques.

6. Review of previous research – cont. • Kalogirou (2006) suggests a model for predicting the energy neededfor building heating, the model is based on artificial neural networks. The input space of the model consists of building characteristics of the building (windows, floors, walls) and the required internal temperature. • Wong et al. (2010) developed a neural network model for predicting the required amount of energy for heating, cooling, lighting and the total amount of energy generated by commercial buildings. • Input space consisted of 9 variables, of which 4 variables are related to meteorological and 4 variables related to constructions data. Authors achived very high accuracy models almost over 94%. • Yu et al. (2010) suggested a building energy demand predictive model based on the decisiontree method. • theyestimate residential building energy performance indexes by modeling building energy use intensity(EUI) levels. • The results demonstrate that the use of decision tree method can classify and predict buildingenergy demand levels accurately (93% for training data and 92% for test data), identify and rank significantfactors of building EUI automatically.

7. Review of previous research – cont. Overviewof previous research show that for thatpurposeauthorshavemostly used statistical methodssuch as linear regression, time series analysis, probability density functions, while others combine or competitively compare statistical methods with machine learning methods or use simulation modelling. In input spacemost of the authors use building physical characteristics in addition to weather data to predict energy consumption, while some authors also use occupation data. • Dong et al. (2005) inorder to predict the monthly energy consumption in four offices in SingaporeusedSVM. • The input variables are the mean outdoor dry-bulb temperature, the relative humidity and the global solar radiation.

8. Dataset • A real dataset obtained from The Agency for Legal Trade and Real Estate Brokerage (APN) in Croatia were used. • The dataset includes dataabout1760public buildingsand142 attributeswhichdescribing their geospatial, construction, heating, cooling, meteorological and energy consumptiondata. • Due to a large numberofvariablesthefirststepwas to reduceit. Highlycorrelatedvariableswerethefirst to belookedinto.

9. Variables used for modelling gas consumption of public buildings expressed in KWh

10. Panel analysis • Variables: • - dependentvariable • - independentvariablesthatchangeover time andindividuals • - independentvariablesthatchangeacrossindivivduals, but are constantin time • - independentvariablesthatchangeover time, but are equal for allindividuals

11. Between panel analysis • Assumptions for randomeffect panel analysis: • Residualsandindependentvariables are uncorrelated • Homoscedacityofresiduals • Teststhatwereconducted:

12. Model – R^2 je 0.9199, a ajd R^2 je 0.75992

13. Samplingandmodelling procedure – kako je s panel analizom???? • In order to obtain systematic training, testing and validation of NNs, the sample is divided into three subsamples: • equal distribution of output variables in the train (60% of cases) and test sample (20% of cases), while the rest of the cases is added to the validation sample (20% of data) • In order to develop a successful ANN model for recognizing an energy efficient level of public buildings and identifying their most important characteristics, followingmodellingprocedurewas suggested:

14. Modelling procedure – ovo je staro ali kao primjer ako možemo iskoristiti

15. Results

16. Results

17. Conclusionandlimitations

18. Conclusionandlimitations – malo opcenito • The model can serve as a baseline for further research in this area, and is somewhat consistent to research in other countries showing that various groups of variables are important. • In order to improve the model accuracy, machine learning methods can be tested, and some other methods for variable reduction. Nešto u tom smislu • Such modelshave economic implications since they could serve as a support for estimating savings in reconstruction measures, and better allocation of state budget and other resources aimed to increase energy efficiency of public buildings.

19. References • Dai, Y-H., 2002. Convergence properties of the BFGS algorithm, SIAM Journal of Optimization, Vol. 13, No. 3, pp. 693-701. • De Wilde, P. (2014). The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in Construction, 41, 40-49. • Dong Bing, Cao Cheng, Lee Siew Eang. Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings 2005;37:545–53. • European Commission, Energy Efficiency Directive, https://ec.europa.eu/energy/en/topics/energy-efficiency/energy-efficiency-directive, [18.6.2017.] • European Investment Bank, The Benefits of Energy Efficiency, http://www.eib.org/epec/ee/documents/factsheets-energy-efficiency-en.pdf, [18.6.2017.] • Jackson, J. (2010). Promoting energy efficiency investments with risk management decision tools. Energy Policy, 38(8), 3865-3873. • Jakob, M. (2006). Marginal costs and co-benefits of energy efficiency investments: The case of the Swiss residential sector. Energy policy, 34(2), 172-187. • Kalogirou, S. A. (2006). Artificial neural networks in energy applications in buildings. International Journal of Low-Carbon Technologies, 1(3), 201-216. • Kneifel, J. (2010). Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings. Energy and Buildings, 42(3), 333-340.

20. References • Martinaitis, V., Kazakevičius, E., & Vitkauskas, A. (2007). A two-factor method for appraising building renovation and energy efficiency improvement projects. Energy Policy, 35(1), 192-201. • Masters, T., 1995. Advanced Algorithms for Neural Networks, A C++ Sourcebook, John Wiley & Sons, Inc., New York, USA. • Paliwal, M. and Kumar U.A., 2009. Neural networks and statistical techniques: A review of applications, Expert Systems with Applications, Vol. 36, pp. 2–17. • Prieto, A., Prieto, B., Martinez Ortigosa, E., Ros, E., Pelayo, F., Ortega, J., Rojas, I. (2016). Neural networks: An overview of early research, current frameworks and new challenges, Neurocomputing, in press, Available online 8 June 2016, ISSN 0925-2312, http://dx.doi.org/10.1016/j.neucom.2016.06.014. • Rumelhart, D. E., Hinton, G.E., Williams, R.J. (1989), Learning resentations by back-propagating errors, in Anderson, J.A. and Rosenfeld, E. (eds.) Neurocomputing: Foundations of Research, MIT Press, A Bradford Book, Cambridge, MA, USA. • Tsanas, A., & Xifara, A. (2012). Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49, 560-567. • Zekić-Sušac, M. (2017). Overview of prediction models for buildings energy efficiency, Proceedings Of The International Scientific Symposium „Economy Of Eastern Croatia – Vision And Growth“, AnkaMašekTonković (Ed.), Osijek, May 25-27, 2017, pp. 697-706. • Zhao, H. X., & Magoulès, F. (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586-3592.

21. Acknowledgments Collaboration institutions: This work has been fully supported by Croatian Science Foundation under Grant No. IP-2016-06-8350 "Methodological Framework for Efficient Energy Management by Intelligent Data Analytics" (MERIDA). The 14th International Symposium on Operations Research in Slovenia | 27th – 29th September 2017, Bled, Slovenia