Can I Shift My Load? Optimizing the Selection of the Best Electrical Tariff for Tertiary Buildings
Sustainability is strongly related to the appropriate use of available resources, being an important cornerstone in any company’s administration due to the direct influence on its efficiency and ability to compete in the global market. Therefore, the intelligent and proper management of these resources is a pressing matter in terms of cost savings. Among the possible alternatives for optimisation, the one regarding electricity consumption stands out due to its strong influence on the expenses account. In general, this type of optimisation can be carried out from two different perspectives: one that concerns the efficient use of energy itself and the other related to the proper adjustment of the electricity contract so that it meets the infrastructure needs while avoiding extra costs derived from poorly sized bills. This paper describes the application of an artificial intelligence based methodology for the optimisation of the parameters contracted in the electricity tariff in the Spanish market. This technique is able to adjust the power term needed so that the global economic cost derived from energy consumption is significantly reduced. The papers discusses the impact that this proposal may have on a demand response scenario associated to load shifting practices within university buildings. Furthermore, the role of human beings, specifically university employees, and their actions towards reducing the overuse of power consumption at the same time is also addressed.
KeywordsDemand response Energy costs Forecasting Flexibility Genetic algorithms
We acknowledge the support of the Spanish government for SentientThings project under Grant No.: TIN2017-90042-R.
- 1.Ratnatunga, J.: Carbon cost accounting: the impact of global warming on the cost accounting profession. J. Appl. Manag. Acc. Res. 5, 01 (2007)Google Scholar
- 5.Zhang, J., et al.: Blockchain based intelligent distributed electrical energy systems: needs, concepts, approaches and vision. Zidonghua Xuebao/Acta Automatica Sinica 43(9), 1544–1554 (2017)Google Scholar
- 7.Wain, N.: Households still baffled by energy bills and vote them the most difficult paperwork to understand despite rules to make them clearer. Thisismoney.co.uk, October 2014Google Scholar
- 9.Borges, C.E., Penya, Y.K., Fernández, I.: Optimal combined short-term building load forecasting. In: 2011 IEEE PES Innovative Smart Grid Technologies, pp. 1–7, November 2011Google Scholar
- 14.Quintal, F., Jorge, C., Nisi, V., Nunes, N.: Watt-I-See: a tangible visualization of energy. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, ser. AVI ’16, pp. 120–127. ACM, New York, NY, USA (2016)Google Scholar
- 15.Sugarman, V., Lank, E.: Designing persuasive technology to manage peak electricity demand in ontario homes. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1975–1984. ACM (2015)Google Scholar
- 18.Reed, G.F., Lynn, F., Meade, B.D.: Use of coefficient of variation in assessing variability of quantitative assays. Clin. Diagn. Lab. Immunol. 9(6), 1235–1239 (2002)Google Scholar