Nowcasting Methods For Optimising Building Performance

Abstract

In meteorology term, nowcasting is weather forecasting for the next few minutes to six hours using all immediately available weather data. It is a relatively new subject, which often involves remote sensing, numerical weather prediction models, and advanced data communication infrastructure. High-quality weather nowcasting is crucial for optimising building performance in the near future. A range of nowcasting techniques has been used for such purposes. It includes statistical, machine learning, Numerical Weather Prediction (NWP), top-down and bottom-up approaches. This paper firstly reviews the advantages and disadvantages of common nowcasting methods with the focus on solar radiation nowcasting. Based on the review, popular methods have been classified into five categories. Authors then investigated further the nowcasting data provided by weather Application Programming Interfaces (APIs) that is backed by Numerical Weather Prediction. This is due to its large-scale application potential and the significances in the most recent update on solar radiation nowcast. Secondly, the paper explores the implications of applying weather nowcasting to dynamic building simulations, most importantly, examining its impact on the accuracy of indoor temperature prediction for free float buildings, heating load prediction and heating energy for heated buildings. The study used three buildings from BESTEST ANSI/ASHRAE Standard 140-2014 as the case studies. The results show that the most recent update of weather API includes meaningful solar radiation prediction. If the building does not have a large south facing glazing, the indoor temperature and heating load predictions from dynamic models are reasonably accurate.

Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data

“…The CV(RMSE) is achieved by weighting the Root Mean Square Error (RMSE) by the mean of the actual data. The measured variability is considered to be error variance by this index and therefore the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) Guideline 14, the Federal Energy Management Program (FEMP) and the International Performance Maintenance and Verification Protocol (IPMVP) recommend its use [30,[35][36][37][38][39][40][41][42]. Secondly, the coefficient of determination R 2 (Equation 2) which is the percentage of variation of the response variable that explains its relationship with one or more predictor variables.…”

Solar radiation nowcasting through advanced CNN model integrated with ResNet structure

“…As the representation of renewable energy sources, solar energy has great potential capacity to building energy management (Guermoui, Melgani, & Danilo, 2018). In building energy management, high spatial-temporal resolution GHI, DNI and DHI are the critical input parameters for achieving high-performance buildings through model predictive control (Du, Bandera, & Chen, 2019). However, accurate prediction of solar radiations at ground level has always been a challenging task considering the dynamic nature of cloud movement, water vapour and pollution of the air.…”

Empirical and Comparative Validation for a Building Energy Model Calibration Methodology

“…The simulation can have errors, even when the real and measured data coincide [ 24 ]. There are many sources of uncertainty, e.g., the weather [ 25 , 26 , 27 ] or the occupation [ 28 ], in this paper the center is the envelope parameters. Many degrees of liberty in the calibration process can produce good agreement with the measured data [ 29 ], but have an error in the individual datum [ 30 , 31 ].…”