Abstract
Fuzzy clustering algorithm has demonstrated advantages in data mining, especially in problems with large collections of imprecise or fuzzy data. This methodology is widely used in the context of pattern recognition. It is proposed this methodology to use in the forecast of the comfort area of building or in the forecasting of consumption. In this paper the concept of fuzzy clustering, which is widely used in the context of pattern recognition. Based on the study of the fuzzy algorithm, we propose a method to forecast the comfort and consumption.
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References
ASHRAE (2013) Standard 55-2013: thermal environmental conditions for human occupancy R. and A. conditioning E. American Society of Heating, ed
Bezdek JC (1974) Numerical taxonomy with fuzzy sets. J Math Biol 1:57–71
Feinberg EA, Genethliou D (2006) Load forecasting. Appl Math Restruct Electr Power Syst:269–285
Humphreys MA, Hancock M (2007) Do people like to feel “neutral”?: exploring the variation of the desired thermal sensation on the ASHRAE scale. Energ Build 39(7):867–874. Available at: http://www.sciencedirect.com/science/article/pii/S037877880700059X
Jain AK, Murthy MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3): 264–323
Nicol F, Humphreys MA, Roaf S (2012) Adaptive thermal comfort: principles and practice. Routledge, New York. Available at: http://dx.doi.org/10.4324/9780203123010
Nicol JF, Humphreys MA (2002) Adaptive thermal comfort and sustainable thermal standards for buildings. Energ Build 34(6):563–572. Available at: http://www.sciencedirect.com/science/article/pii/S0378778802000063
Zhao J et al (2014) Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining. Energ Build 82:341–355
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Aparicio-Ruiz, P., Martín, E.B., Martín, J.G., Achedad, P.C. (2019). Short-Term Forecasting in Office Consumption with Identification of Patterns by Clustering. In: Ortiz, Á., Andrés Romano, C., Poler, R., García-Sabater, JP. (eds) Engineering Digital Transformation. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-96005-0_24
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DOI: https://doi.org/10.1007/978-3-319-96005-0_24
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