Effectiveness of Using Predicted Solar Radiation Data in Building Performance Simulation

Authors

  • Hany Gaballa North Carolina State University
  • Soolyeon Cho North Carolina State University

Keywords:

Artificial Neural Network, heating and cooling loads, solar radiation prediction

Abstract

In real-time building performance simulation, real-time weather data is required. Solar radiation information is one of the most important weather parameters; however, it is not readily available. This paper presents an Artificial Neural Network algorithm that predicts global solar radiation based on easily accessible weather data; i.e., temperature and humidity. Diffuse and direct normal solar radiations are generated from predicted global solar radiation using the EnergyPlus™ weather converter program, which is also used as a weather packing tool to create the EPW weather file for EnergyPlus simulation. An office building is used as a case study for analysis. Three simulation scenarios are developed using: (1) complete Typical Meteorological Year (TMY3) file, (2) Limited_TMY3 file, and (3) Predicted _TMY3 file. This study analyzes the feasibility of using the predicted solar radiation data in the building performance simulation. The simulation results of three different scenarios are compared and analyzed.

Author Biographies

Hany Gaballa, North Carolina State University

Education:

  • * Master in Architecture Engineering, Military Technical College, MTC, Cairo, Egypt, November 2014, with courses GPA of 4, it was titled; “Architectural Treatments of the Military Constructions in Hot Arid Zones Using the Modern Energy Technologies”.
  • * Bachelor in Architecture Engineering, Military Technical College, Cairo, Egypt, June 2006, with overall grade of “Excellent with honors “, and El-Alameen Museum Development as graduation design project with grade “Excellent”, B.Arch. GPA. 3.57 of 4 and was ranked the 1st among graduate colleagues.

 

Skills:

  • * DesginBuilder – indoor climate simulations and analysis for comfort, energy consumption and CO2 emissions
  • * ENVI-met – numerical simulations for urban form thermal impact assessment and pedestrian comfort calculations
  • * Grasshopper/Rheino for Parametric design
  • * CCWorldWeatherGenTool for morphing weather data to predicting and assessing future urban developments’ scenarios
  • * 3ds Max – AutoCAD drawing programs
  • * Photoshop

Soolyeon Cho, North Carolina State University

Dr. Cho joined the architecture faculty at NC State University in 2011. He began his academic career at The Catholic University of America in Washington DC where he taught building energy and technology courses in the Master of Science in Sustainable Design (MSSD) program for three years.

Dr. Cho’s expertise is in the energy modeling and performance simulation for the design and development of sustainable buildings. His teaching includes Energy Modeling & Simulation, Energy Efficiency & Renewable Energy, and Building Energy Optimization. His research and work experience includes energy savings calculation, high-performance building design, energy-efficient systems design, renewable energy systems integration, and performance Measurement and Verification (M&V).

Dr. Cho’s research is interdisciplinary, dealing with both architectural, environmental, and engineering subjects for the design and development of sustainable built environment. He extensively utilizes energy modeling and simulation technologies, especially using one of the most advanced programs such as EnergyPlus developed by US DOE. The use of the technology is to predict potential energy benefits from the early design phases through the life of a building, which can help make optimal design/development decisions in terms of energy efficiency, while maintaining the same or better occupant comfort conditions at the same time. Also, Dr. Cho utilizes Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to develop algorithms for optimal controls of HVAC and energy systems.

Currently, Dr. Cho is the Director of the Building Energy Technology Lab (BETlab). BETlab is a research group concerned with Building Energy Technologies and Design Issues related to Energy Efficiency & Renewable Energy in buildings for the development of a Sustainable Built Environment.

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Published

2020-05-25

Issue

Vol. 4 (2020): Buildings, Cities, and Performance II

Section

Articles