Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

92 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1740-1753
Journal / PublicationApplied Energy
Volume228
Online published19 Jul 2018
Publication statusPublished - 15 Oct 2018

Abstract

Short-term load prediction, which forecasts a building's thermal load with a lead time ranging from seconds to a few days, is essential for not only monitoring and controlling the system operation, but also on-line scheduling. Dynamic cooling load forecasting, which belongs to short-term load prediction, is both meaningful for monitoring the system or fuzzy on-line scheduling and crucial for solving the time-lag problem to meet the heating, ventilation and air-conditioning system's time-varying cooling loads. Numerous studies have been carried out to develop dynamic load-forecasting models, and great achievements have been made. However, limitations in their applicability persist because most previous models are calendar- and time-based data-driven models that may fail when unexpected issues occur or special schedules are adopted. What's more, the inputs that were selected passively from the source data pools at hand rather than via active exploration may be insufficient and impair the accuracy of forecasting models. This paper proposes a novel dynamic forecasting model for building cooling loads that combines an artificial neural network with an ensemble approach. Based on physical principles other than the available data source, the inputs are explored actively and are independent from both calendar and time indicators, which make the forecasting model being capable of dealing with irregular occasions and unexpected schedules with high accuracy. A benchmark is proposed that uses the current load Q(t) as a forecasted cooling load Q^(t+i) and gives the minimum accuracy requirement for a dynamic forecasting model. The benchmark not only can be used to evaluate dynamic forecasting models that are validated by various case studies, but also ensures that the proposed forecasting model can be applied immediately to heating, ventilation and air-conditioning systems to tackle the time-lag problem.

Research Area(s)

  • Artificial neural network, Building cooling loads, Dynamic load forecasting, Ensemble approach