Skip to main content

Advertisement

SpringerLink
Log in

Forecasting power consumption for higher educational institutions based on machine learning

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Electric power consumption is affected by diverse factors. In particular, a university campus, which is one of the highest power consuming institutions, shows a wide variation of electric load depending on time and environment. For stable operation of such institution, reliable electric power supply should be guaranteed. One of possible methods to do that is to forecast the electric load accurately and supply power accordingly. Even though various influencing factors of power consumption have been discovered for educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative forecasting of their electric load. In this paper, we build a power consumption forecasting model using various machine learning algorithms. To evaluate their effectiveness, we consider four building clusters in a university and collect their power consumption data of 15-min interval over more than one year. For the data, we first extract features based on the periodic characteristic and then perform the principal component analysis and factor analysis for the features. We build two electric load forecasting models using artificial neural network and support vector regression. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to actual electric load. The experimental results show that the two forecasting models can achieve average error rate of 3.46–10 % for all clusters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sustain Energy Rev 33:102–109

    Article  Google Scholar 

  2. Raza MQ, Khosravi A (2015) A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev 50:1352–1372

    Article  Google Scholar 

  3. Hernandez L, Baladron C, Aguiar JM, Carro B, Sanchez-Esguevillas AJ, Lloret J, Massana J (2014) A survey on electric power demand forecasting: future trends in smart grids, microgrids and smart buildings. IEEE Commun Surv Tutor 16(3):1460–1495

    Article  Google Scholar 

  4. Wi YM, Kong S, Lee J, Joo SK (2016) Demand-side management program planning using stochastic load forecasting with extreme value theory. J Electr Eng Technol 11(5):1093–1099

    Article  Google Scholar 

  5. Sandels C, Widen J, Nordstrom L, Andersson E (2015) Day-ahead predictions of electricity consumption in a Swedish office building from weather, occupancy, and temporal data. Energy Build 108:279–290

    Article  Google Scholar 

  6. Pascual J, Barricarte J, Sanchis P, Marroyo L (2015) Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting. Appl Energy 158:12–25

    Article  Google Scholar 

  7. Powell KM, Sriprasad A, Cole WJ, Edgar TF (2014) Heating, cooling, and electrical load forecasting for a large-scale district energy system. Energy 74:877–885

    Article  Google Scholar 

  8. Chung MH, Rhee EK (2014) Potential opportunities for energy conservation in existing buildings on university campus: A field survey in Korea. Energy Build 78:176–182

    Article  Google Scholar 

  9. Son H, Kim C (2016) “Short-term forecasting of electricity demand for the residential sector using weather and social variables,” Resources, conservation and recycling

  10. Li K, Hu C, Liu G, Xue W (2015) Building’s electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy Build 108:106–113

    Article  Google Scholar 

  11. Li K, Su H, Chu J (2011) Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study. Energy Build 43(10):2893–2899

    Article  Google Scholar 

  12. Bagnasco A, Fresi F, Saviozzi M, Silvestro F, Vinci A (2015) Electrical consumption forecasting in hospital facilities: an application case. Energy Build 103:261–270

    Article  Google Scholar 

  13. Grolinger K, L’Heureux A, Capretz MA, Seewald L (2016) Energy forecasting for event venues: Big data and prediction accuracy. Energy Build 112:222–233

    Article  Google Scholar 

  14. Chitsaz H, Shaker H, Zareipour H, Wood D, Amjady N (2015) Short-term electricity load forecasting of buildings in microgrids. Energy Build 99:50–60

    Article  Google Scholar 

  15. Amber KP, Aslam MW, Hussain SK (2015) Electricity consumption forecasting models for administration buildings of the UK higher education sector. Energy Build 90:127–136

    Article  Google Scholar 

  16. Ghelardoni L, Ghio A, Anguita D (2013) Energy load forecasting using empirical mode decomposition and support vector regression. IEEE Trans Smart Grid 4(1):549–556

    Article  Google Scholar 

  17. Jurado S, Nebot À, Mugica F, Avellana N (2015) Hybrid methodologies for electricity load forecasting: entropy-based feature selection with machine learning and soft computing techniques. Energy 86:276–291

    Article  Google Scholar 

  18. John Lu ZQ (2010) The elements of statistical learning: data mining, inference, and prediction. J R Stat Soc Ser A (Stat Soc) 173(3):693–694

    Article  Google Scholar 

  19. Schalkoff RJ (1997) Artificial neural networks. McGraw-Hill Higher Education, New York

    MATH  Google Scholar 

  20. Jovanović RŽ, Sretenović AA, Živković BD (2015) Ensemble of various neural networks for prediction of heating energy consumption. Energy Build 94:189–199

    Article  Google Scholar 

  21. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  22. Vapnik V (2006) Estimation of dependences based on empirical data. Springer, Berlin

    MATH  Google Scholar 

  23. Dong B, Cao C, Lee SE (2005) Applying support vector machines to predict building energy consumption in tropical region. Energy Build 37(5):545–553

    Article  Google Scholar 

  24. Li Q, Meng Q, Cai J, Yoshino H, Mochida A (2009) Applying support vector machine to predict hourly cooling load in the building. Appl Energy 86(10):2249–2256

    Article  Google Scholar 

  25. Vapnik Vladimir (2013) The nature of statistical learning theory. Springer, Berlin

    MATH  Google Scholar 

  26. Jain RK, Smith KM, Culligan PJ, Taylor JE (2014) Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Appl Energy 123:168–178

    Article  Google Scholar 

  27. Zhao HX, Magoulès F (2012) Feature selection for predicting building energy consumption based on statistical learning method. J Algorithms Comput Technol 6(1):59–77

    Article  Google Scholar 

  28. Honaker J, King G, Blackwell M (2011) Amelia II: a program for missing data. J Stat Softw 45(7):1–47

    Article  Google Scholar 

  29. Aitkin M, Wilson GT (2012) Mixture models, outliers, and the EM algorithm. Technometrics 22(3):325–331

    Article  MATH  Google Scholar 

  30. Climate of Seoul. https://en.wikipedia.org/wiki/Climate_of_Seoul. 22 Nov 2016

  31. Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459

    Article  Google Scholar 

  32. Kaiser HF (1960) The application of electronic computers to factor analysis. Educ Psychol Meas 20(1):141–151

    Article  Google Scholar 

  33. Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688

    Article  Google Scholar 

  34. Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  35. Schaul T et al (2010) PyBrain. J Mach Learn Res 11:743–746

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20152010103060).

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Korea University, Seoul, Korea

    Jihoon Moon, Jinwoong Park & Eenjun Hwang

  2. Seerslab, Seoul, Korea

    Sanghoon Jun

Authors
  1. Jihoon Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinwoong Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eenjun Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanghoon Jun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eenjun Hwang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moon, J., Park, J., Hwang, E. et al. Forecasting power consumption for higher educational institutions based on machine learning. J Supercomput 74, 3778–3800 (2018). https://doi.org/10.1007/s11227-017-2022-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-017-2022-x

Keywords

Search

Navigation