Predictive artificial neural network models to forecast the seasonal hourly electricity consumption for a University Campus

Abstract This paper proposes artificial neural network (ANN) models to forecast the seasonal hourly electricity consumption for three areas of a university campus, Japan. A total of six parameters including day of week, hour of day, hourly dry-bulb temperature, hourly relative humidity, hourly global irradiance, and previous hourly electricity consumption are used as input variables. The ANN models are developed to predict the future seasonal hourly electricity consumption for the three areas, considering the Feed-forward ANN trained with the Levenberg-Marquardt (LM) back-propagation algorithms. The correlation coefficient (R2) and root mean square error (RMSE) metrics are adopted to evaluate the accuracy of proposed ANN models. It showed that the R2 between actual measurement and ANN models prediction ranges between 0.96 and 0.99 at training stage, and between 0.95 and 0.99 at testing stage. It showed that RMSE in Science and Technology area of the university campus is the largest among three areas, followed by Humanities College area. The Old Liberal Arts area has the smallest RMSE and its difference. As future work, more input variables such as class schedule and indoor human activities etc. are suggested to be added to the input layer of ANN models for improving their forecast accuracy.

[1]  Carlos Cardeira,et al.  Neural Networks Applied to Short Term Load Forecasting: A Case Study , 2017 .

[2]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[3]  Dongwei Yu,et al.  A two-step approach to forecasting city-wide building energy demand , 2018 .

[4]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[5]  C.A.L. Bailer-Jones,et al.  An introduction to artificial neural networks , 2001 .

[6]  Yacine Rezgui,et al.  Computational intelligence techniques for HVAC systems: A review , 2016, Building Simulation.

[7]  Yacine Rezgui,et al.  Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption , 2017 .

[8]  Peter J. G. Pearson,et al.  A low carbon industrial revolution? Insights and challenges from past technological and economic transformations , 2012 .

[9]  Jihui Yuan,et al.  Development and application of a simple BEMS to measure energy consumption of buildings , 2015 .

[10]  Jihui Yuan,et al.  Inter-Building Effect and Its Relation with Highly Reflective Envelopes on Building Energy Use: Case Study for Cities of Japan , 2017 .

[11]  Yacine Rezgui,et al.  A Smart Forecasting Approach to District Energy Management , 2017 .

[12]  Yusuke Nakajima,et al.  ENERGY CONSUMPTION SURVEY IN A HIGH-RISE UNIVERSITY , 2014 .

[13]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

[14]  Yoshiyuki Shimoda,et al.  ESTIMATION OF ENERGY END-USE BREAKDOWN IN SCIENCE RESEARCH FACILITIES , 2014 .

[15]  Jiejin Cai,et al.  Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .

[16]  Abdullatif Ben-Nakhi,et al.  Cooling load prediction for buildings using general regression neural networks , 2004 .

[17]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[18]  Hecht-Nielsen.  Theory of the backpropagation neural network , 1989 .

[19]  Jesús M. Zamarreño,et al.  Prediction of hourly energy consumption in buildings based on a feedback artificial neural network , 2005 .

[20]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[21]  Saburo Murakawa,et al.  STUDY ON THE ENERGY CONSUMPTION OF AGED WELFARE FACILITIES WITH HABITATION IN THE CHUGOKU AND SHIKOKU REGIONS , 2014 .