A middleware platform for the validation and utilization of short-term weather forecast data for office buildings

Abstract Simulations are widely used to calculate the thermal environment and energy consumption of buildings. The results of these simulations are affected by weather data, thus making the selection of appropriate weather data essential. Typical weather data are used to analyze a building’s thermal performance, but this is not appropriate for analyzing the performance of an actual building controlled by a building energy management system, which responds to real-time weather conditions. Such a building needs short-term weather forecast data to calculate the energy consumption. However, an evaluation of the validity of weather forecast data has not been performed. This study quantitatively analyzes the validation of real-time weather forecast data. A middleware platform was developed to combine weather forecast data and the EnergyPlus software using Grasshopper software. In addition, weather forecast data and actual weather data were compared to evaluate the forecast validation and a predictive control method using weather forecast data was devised. The application of real-time control was found to reduce the electricity costs incurred for cooling by 10% relative to there being no control, and by 2% relative to fixed temperature control.

[1]  Mostapha Sadeghipour Roudsari,et al.  AN INNOVATIVE WORKFLOW FOR BRIDGING THE GAP BETWEEN DESIGN AND ENVIRONMENTAL ANALYSIS , 2013 .

[2]  Lingling Zhang,et al.  Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China , 2016 .

[3]  Peng Xu,et al.  An alternative method to predict future weather data for building energy demand simulation under global climate change , 2016 .

[4]  Elie Azar,et al.  Integrating building performance simulation in agent-based modeling using regression surrogate models: A novel human-in-the-loop energy modeling approach , 2016 .

[5]  A. Chan.  Developing future hourly weather files for studying the impact of climate change on building energy , 2011 .

[6]  Farshad Kowsary,et al.  Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO) , 2016 .

[7]  Jae-Weon Jeong,et al.  Optimization of a free-form building shape to minimize external thermal load using genetic algorithm , 2014 .

[8]  D. B. Crawley,et al.  Does it matter which weather data you use in energy simulations , 1996 .

[9]  Ana Paula de Almeida Rocha,et al.  On the uncertainty assessment of incident direct solar radiation on building facades due to shading devices , 2016 .

[10]  Ismael R. Maestre,et al.  Reduction of computation time in building energy performance simulation programs by applying tearing techniques , 2016 .

[11]  Ramkishore Singh,et al.  Uncertainty and sensitivity analyses of energy and visual performances of office building with external venetian blind shading in hot-dry climate , 2016 .

[12]  Junjie Li,et al.  Multi-criteria approach to passive space design in buildings: Impact of courtyard spaces on public buildings in cold climates , 2015 .

[13]  Tianzhen Hong,et al.  A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data , 2013 .

[14]  D. Thevenard,et al.  Ground reflectivity in the context of building energy simulation , 2006 .

[15]  Cheol-Yong Jang,et al.  Feasibility study on a novel methodology for short-term real-time energy demand prediction using weather forecasting data , 2013 .

[16]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[17]  Jan Hensen,et al.  Building Performance Simulation for Design and Operation , 2019 .

[18]  Steffen Petersen,et al.  The effect of weather forecast uncertainty on a predictive control concept for building systems operation , 2014 .

[19]  Rasmus Lund Jensen,et al.  Building simulations supporting decision making in early design – A review , 2016 .

[20]  Jean-Jacques Roux,et al.  Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass , 2015 .

[21]  Tetsuo Hayashi,et al.  PROCEDURES FOR SEPARATING DIRECT AND DIFFUSE INSOLATION ON A HORIZONTAL SURFACE AND PREDICTION OF INSOLATION ON TILTED SURFACES , 1983 .

[22]  David Rutten,et al.  Galapagos: On the Logic and Limitations of Generic Solvers , 2013 .

[23]  Donghyun Seo.  Development of a universal model for predicting hourly solar radiation---Application: Evaluation of an optimal daylighting controller , 2010 .

[24]  Akbar Maleki,et al.  Weather forecasting for optimization of a hybrid solar-wind–powered reverse osmosis water desalination system using a novel optimizer approach , 2016 .

[25]  Juha Jokisalo,et al.  Cost-optimal energy performance renovation measures of educational buildings in cold climate , 2016 .