# Machine Learning for Forecasting Building System Energy Consumption

## Abstract

Today, the use and demand response of electric power has become increasingly important in industry, transportation, commercial and residential buildings. to better optimize the consumption of electrical energy and avoid as much as possible the loss of this source and bring a benefit for energy operators as well as for the end user, the prediction of electric energy with the use of the AI artificial intelligence science and the different prediction methods that have emerged under the field of machine learning namely ANN, SVM… becomes promoter and quite paramount. The purpose of this article is to introduce the principle and the relationship of ML and smart grid and to make a study of the state of the art of current electrical energy prediction methods and to compare all methods based on the latest proven research in the field of energy prediction at building level. In this paper the comparison and study is about measuring the accuracy of prediction with a low error percentage. There are also hybrid prediction methods that are improved over the single methods, also a new approach is proposed in this paper called SMEP (Smart Model of Electric Power Prediction).

## Keywords

Smart grid ANN Prediction Forecasting Machine learning methods Hybrid methods## References

- 1.Hong, T.: Short Term Electric Load Forecasting. North Carolina State University, Raleigh (2010)Google Scholar
- 2.Dauta, M.A.M., Hassanm, M.Y., Abdullaha, H., Rahmana, A.H., Abdullaha, M.P., Hussin, F.: Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: a reviewGoogle Scholar
- 3.Fallah, S.N.: Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions (2018)CrossRefGoogle Scholar
- 4.Mehtaa, P., et al.: A high-bias, low-variance introduction to Machine Learning for physicists 1.1. What is Machine Learning?Google Scholar
- 5.Mehtaa, P., et al.: A high-bias, low-variance introduction to Machine Learning for physicists, 1.3. Scope and structure of the reviewGoogle Scholar
- 6.World Economic Forum Accelerating Smart Grid InvestmentsGoogle Scholar
- 7.Article Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research DirectionsGoogle Scholar
- 8.Article Machine Learning for the New York City Power GridGoogle Scholar
- 9.Fan, Z., Chen, Q., Kalogridis, G., Tan, S., Kaleshi, D.: The power of data: data analytics for M2 M and smart grid. In: Proceedings of 3rd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe), pp. 1–8 (2012)Google Scholar
- 10.Diamantoulakis, P.D., Kapinas, V.M., Karagiannidis, G.K.: Big data analytics for dynamic energy management in smart gridsGoogle Scholar
- 11.Mirowski, P., Chen, S., Ho, T.K., Yu, C.-N.: Demand forecasting in smart grids. Bell Labs Tech. J.
**18**(4), 135–158 (2014)CrossRefGoogle Scholar - 12.Mallik, R., Sarda, N., Kargupta, H., Bandyopadhyay, S.: Distributed data mining for sustainable smart grids. In: Proceedings of ACM SustKDD 2011, pp. 1–6 (2011)Google Scholar
- 13.Zhou, K.L., Yang, S.L., Shen, C.: A review of electric load classification in smart grid environment. Renew. Sustai. EnergyRev.
**24**, 103–110 (2013)CrossRefGoogle Scholar - 14.Vale, Z., Morais, H., Ramos, S., Soares, J., Faria, P.: Using data mining techniques to support DR programs definition in smart grids. In: Proceedings of IEEE Power and Energy Society General Meeting, pp. 1–8 (2011)Google Scholar
- 15.Diamantoulakisb, P.D., Kapinasb, V.M., Karagiannidis, G.K.: Big data analytics for dynamic energy management in smart gridsGoogle Scholar
- 16.Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R.X.: Deep learning and its applications to machine health monitoringGoogle Scholar
- 17.Li, H.-Z., Guo, S., Li, C.-J., Sun, J.-Q.: A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl.-Based Syst.
**37**, 378–387 (2013)CrossRefGoogle Scholar - 18.Ling, S.-H., Leung, F.H.F., Lam, H.K., Lee, Y.-S., Tam, P.K.S.: A novel genetic-algorithm-based neural network for short-term load forecasting. IEEE Trans. Ind. Electr.
**50**, 793–799 (2003)CrossRefGoogle Scholar - 19.Zeng, M., Xue, S., Wang, Z., Zhu, X., Zhang, G.: Short-term load forecasting of smart grid systems by combination of general regression neural network and least squares-support vector machine algorithm optimized by harmony search algorithm method. Appl. Math.
**7**, 291–298 (2013)Google Scholar - 20.Hong, W.-C.: Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Convers. Manag.
**50**, 105–117 (2009)CrossRefGoogle Scholar - 21.Jiang, H., Zhang, Y., Muljadi, E., Zhang, J., Gao, W.: A short term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization. IEEE Trans. Smart Grid
**9**(4), 3341–3350 (2016)CrossRefGoogle Scholar