Machine Learning Based Short Term Load Estimation in Commercial Buildings

Abstract

Nowadays, there are many problems with the electricity system, such as increasing consumption, short-time overload during the intra-day, environmental problems caused by fossil fuel, and foreign-source dependency. Therefore, it is necessary to meet these increasing energy needs, minimize environmental impacts, and develop cost optimization solutions. In order to meet these requirements, it is necessary for the network to have a more dynamic structure and to have real-time monitoring and control systems. Furthermore, to develop the aforementioned system, it is necessary to estimate the load of the users in the system. Therefore, the developed artificial neural network-based load estimation algorithm is capable of high accuracy load estimates, and high precision data were obtained for use in the demand side management system

Keywords

• Short term load prediction
• Machine Learning
• Artificial Neural Network
• Demand Side Management

Machine Learning Based Short Term Load Estimation in Commercial Buildings

Abstract

Nowadays, there are many problems with the electricity system, such as increasing consumption, short-time overload during the intra-day, environmental problems caused by fossil fuel, and foreign-source dependency. Therefore, it is necessary to meet these increasing energy needs, minimize environmental impacts, and develop cost optimization solutions. In order to meet these requirements, it is necessary for the network to have a more dynamic structure and to have real-time monitoring and control systems. Furthermore, to develop the aforementioned system, it is necessary to estimate the load of the users in the system. Therefore, the developed artificial neural network-based load estimation algorithm is
capable of high accuracy load estimates, and high precision data were obtained for use in the demand side management system. 

Keywords

• Short term load prediction
• Machine Learning
• Artificial Neural Network
• Demand Side Management

References

1. Aghajani, G. R., Shayanfar, H. A., & Shayeghi, H. (2017). Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy. https://doi.org/10.1016/j.energy.2017.03.051
2. Bendaoud, N. M. M., & Farah, N. (2020). Using deep learning for short-term load forecasting. Neural computing and applications, 32(18), 15029-15041.
3. Berriel, R. F., Lopes, A. T., Rodrigues, A., Varejao, F. M., & Oliveira-Santos, T. (2017, May). Monthly energy consumption forecast: A deep learning approach. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 4283-4290). IEEE.
4. Bruno, S., Dellino, G., La Scala, M., & Meloni, C. (2018). A Microforecasting Module for Energy Consumption in Smart Grids. Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018. https://doi.org/10.1109/EEEIC.2018.8494345
5. Chitalia, G., Pipattanasomporn, M., Garg, V., & Rahman, S. (2020). Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks. Applied Energy, 278, 115410.
6. Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., ... & Wang, K. (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 195, 659-670.
7. Chernykh, I., Chechushkov, D., & Panikovskaya, T. (2014). The prediction of electric energy consumption using an artificial neural network. WIT Transactions on Ecology and the Environment, 190 VOLUME 1, 109–117. https://doi.org/10.2495/EQ140121
8. Fujiwara, T., & Ueda, Y. (2018). Load forecasting method for Commercial facilities by determination of working time and considering weather information. 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018, 5, 336–341. https://doi.org/10.1109/ICRERA.2018.8567019

9. Huang, J., Jin, H., Xie, X., & Zhang, Q. (2007). Using NARX neural network based load prediction to improve scheduling decision in grid environments. In Third International Conference on Natural Computation (ICNC 2007) (Vol. 5, pp. 718-724). IEEE.
10. Kim, J., Moon, J., Hwang, E., & Kang, P. (2019). Recurrent inception convolution neural network for multi short-term load forecasting. Energy and buildings, 194, 328-341.
11. Li, C., Ding, Z., Zhao, D., Yi, J., & Zhang, G. (2017). Building energy consumption prediction: An extreme deep learning approach. Energies, 10(10), 1525.
12. Kuo, P. H., & Huang, C. J. (2018). A high precision artificial neural networks model for short-term energy load forecasting. Energies, 11(1), 213.
13. Ma, J., Qin, J., Salsbury, T., & Xu, P. (2012). Demand reduction in building energy systems based on economic model predictive control. Chemical Engineering Science, 67(1), 92–100. https://doi.org/10.1016/j.ces.2011.07.052
14. Mena, R., Rodríguez, F., Castilla, M., & Arahal, M. R. (2014). A prediction model based on neural networks for the energy consumption of a bioclimatic building. Energy and Buildings, 82, 142–155. https://doi.org/10.1016/j.enbuild.2014.06.052
15. Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91-99.
16. Molavi, H., & Ardehali, M. M. (2016). Utility demand response operation considering day-of-use tariff and optimal operation of thermal energy storage system for an industrial building based on particle swarm optimization algorithm. Energy and Buildings, 127, 920–929. https://doi.org/10.1016/j.enbuild.2016.06.056
17. Owda, H. M. H., Omoniwa, B., Shahid, A. R., & Ziauddin, S. (2014). Using Artificial Neural Network Techniques for Prediction of Electric Energy Consumption. Retrieved from http://arxiv.org/abs/1412.2186
18. Rahman, A., Srikumar, V., & Smith, A. D. (2018). Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Applied energy, 212, 372-385.
19. Saatwong, P., & Suwankawin, S. (2016). Short-term electricity load forecasting for Building Energy Management System. 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2016. https://doi.org/10.1109/ECTICon.2016.7561477
20. Salahat, S. (2017). Short-Term Forecasting of Electricity Consumption in Palestine Using Artificial Neural Networks. International Journal of Artificial Intelligence & Applications, 8(2), 11–21. https://doi.org/10.5121/ijaia.2017.8202
21. Xu, L., Li, C., Xie, X., & Zhang, G. (2018). Long-short-term memory network based hybrid model for short-term electrical load forecasting. Information, 9(7), 165.
22. Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017). A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renewable and Sustainable Energy Reviews, 73, 1104-1122.