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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. [ABSTRACT FROM AUTHOR]

Highlights: [•] Application of heat control strategy reduces total energy consumption of LED lighting. [•] Convective heat from LED lighting should be emitted outdoors during cooling period. [•] Seasonal optimization of convective heat lowers total energy consumption. [ABSTRACT FROM AUTHOR]