Papers by Oluwaseyi Ogunsola
Heating and cooling loads are the major reasons for energy use in buildings. Buildings are usuall... more Heating and cooling loads are the major reasons for energy use in buildings. Buildings are usually subject to schedules and set-points which are not optimized in response to the dynamic weather conditions, internal loads, and occupancy patterns. The thermal network model has been widely applied for realtime building load estimation, which is crucial for optimizing the operation of the HVAC system. However, there has been limited exploration of the capabilities of the thermal network model due to constraints imposed by the solution method adopted. In this paper, the exponential matrix method was adopted to simplify the state space equations and solve the thermal network model analytically. This enhances the applications of a simplified thermal network model for investigation of multiple scenarios of HVAC system operations and equipment sizing, and for more accurate estimation of heating and cooling loads.
This study also proves that the analytical solution method is symptotically stable regardless of time step. A typical office was used as a case study and the predicted building loads are compared with measured data and numerical results from EnergyPlus. For the case study, the model demonstrated better accuracy and is seen to be robust for thermal load estimation for cooling season.
Heating and cooling load calculations are critical to size Heating, Ventilating and Air condition... more Heating and cooling load calculations are critical to size Heating, Ventilating and Air conditioning (HVAC) systems and determine energy use of their operations. The ASHRAE recommended heating load calculation model is most commonly used for heating load calculations. It adopts a simplified approach by considering only steady-state conductive heat transfer. However, due to thermal storage effect, heat generated in daytime may still be stored in buildings and released at a later time. Such assumption leads to significantly over-sized heating systems which are usually accompanied by high initial cost and higher cost of energy use. This study therefore examines the thermal response and passive storage characteristic of heavy construction for typical office building in continental US states. By allowing space air to drift to reasonably lower values, buildings need to be warmed up before being occupied in the morning. The worst case conditions might happen during warm-up or beginning of occupied hours. This paper evaluates the optimal size of heating system which satisfies thermal comfort while taking advantage of passive thermal storage. Results show varying downsizing opportunities ranging from 14% to 54% across the US. These results have the potential of establishing new heating device design standards for certain climate classifications
Energy and Buildings, 2014
We modeled a thermal zone in a typical office using three different constructions.
Volume 11: Emerging Technologies, 2013
ABSTRACT Buildings are responsible for at least 40% of energy use in most countries of the world,... more ABSTRACT Buildings are responsible for at least 40% of energy use in most countries of the world, and for up to 21% of greenhouse gas emissions globally. As this trend continues, real-time building load measurements are essential for dynamic load response control, understanding and improvement of load distributions and profiles, and for climate-responsive design, particularly in commercial buildings. The focus in this paper is the cooling load, which is the rate at which heat must be removed from the controlled zone to maintain the desired temperature. Estimation of maximum cooling load is necessary for sizing of cooling equipments. However, details needed for whole-building simulation are often unreliable or unavailable. As such, simplified models with reasonable accuracy and computational requirements are often used. A cyber-physical system, integration of physical sensors and mathematical model, is proposed in this paper for cooling load estimation. The physical sensor measurements are limited to outside air temperature, solar radiation, room air temperature, and building plug load. Meanwhile, resistance-capacitance (RC) concept was adopted to describe the physics and dynamics of the building envelope for its simplicity and reasonable computational requirements. The cyber-physical system was tested using a typical office having two thermal zones and compared with simulation results from EnergyPlus, a whole building simulation program. Phenomenon such as infiltration, inter-zone air mixing, and air moisture control were not taken into account for the model. Results are presented to determine the accuracy of the simplified model for cooling load estimation.
Volume 7: Fluids and Heat Transfer, Parts A, B, C, and D, 2012
ABSTRACT Heating and cooling loads which are compensated by heating, ventilation, and air-conditi... more ABSTRACT Heating and cooling loads which are compensated by heating, ventilation, and air-conditioning (HVAC) systems, are the main reason for energy uses in buildings. Energy utilized by HVAC system accounts for two-thirds of a building’s total energy consumption. Excessive energy is consumed when HVAC systems fail to operate as intended. This is often due to several factors such as inappropriate monitoring and control strategy, lack of understanding of the dynamics of thermal loads, and system complexity. Amidst several models, estimation of cooling load using Resistance Capacitance (RC) models have proved to provide more robust and accurate estimates of the building load based on measured data but the use of this method is not without challenges. This study aims to highlight common challenges associated with implementation of the RC method for thermal modeling of cooling load. Past and current research have handled some of the challenges by introducing simplifying assumptions which if not adequately selected can lead to significant deviation between model performance and measured data. Without proper understanding of the challenges, engineers may not be able to place a high degree of confidence in load calculation methods and the computer implementations that they use.
Energy and Buildings, 2014
ABSTRACT Solar radiation is an important climatic variable and widely used in building performanc... more ABSTRACT Solar radiation is an important climatic variable and widely used in building performance monitoring and analysis. However, due to sensor malfunction, data transmission problems, and quality assurance issues, there are often short-term or long-term missing data on solar radiation. These gaps are challenging for engineers involved in building performance monitoring and control. This paper examines and compares three different approaches, namely, singular spectrum analysis (SSA), statistically adjusted solar radiation (SASR), and the temperature-based approach (TBA), for restoring missing solar data. The TBA, SASR, and SSA are applied to fill artificial gaps which are generated continuously as representative of up to 25 days missing data in actual hourly solar radiation data of Oklahoma City North (OKCN) for 2012 and Albuquerque for 2005. Results show that SSA outperforms the other methods for filling solar radiation gaps of up to 5 days. For gaps up to 20 days, the SSA and TBA have similar performance. For gaps larger than 20 days, the TBA is more suitable. The SASR performs similarly to the TBA for dry and sunny Alburquerque climates, but worse in OKCN. Accuracy of the SSA decreases with increasing gap lengths. The study concludes by recommending appropriate methods for different gap lengths.
HVAC&R Research, 2014
The lack of standard procedures for filling climatic data has the potential to undermine design, ... more The lack of standard procedures for filling climatic data has the potential to undermine design, monitoring, and control efforts aimed at climate-responsive building design, performance monitoring, and energy efficiency. This paper addresses the challenge of long-term missing gaps in dry-bulb temperature data by examining three spatial methods, namely the inverse distance weighting (IDW) method, the spatial regression test (SRT) method, and the substitution with best match data (SSBM) method, as well as two temporal methods, namely the temporal regression test (TRT) method and the temporal substitution with best match data (TSBM) method. Using these methods, missing dry-bulb temperature data with long-term gaps ranging from one day up to 60 days are restored, for use in building performance monitoring and analysis. Three one-year hourly data sets were used to evaluate the performance of these approaches. Each method was applied to deal with artificial gaps which were generated randomly and represented different seasons of a year. In terms of the difference between estimated values and measured values, three evaluation indices, namely MAE, RMSE, and STDBIAS, were utilized. The comparison results show that spatial methods are better than temporal methods. The confidence level of the SRT method was further investigated by applying this method to existing data and missing data, and examining its performance. The results indicate that the uncertainty of the SRT method can be predicted and at least two neighboring stations are recommended when using it. This is the second part of the research results obtained through the ASHRAE 1413 research project with a focus on introducing gap-filling methods for long-term gaps in dry-bulb temperature. Downloaded by [University of Oklahoma Libraries], [Li Song]
HVAC&R Research, 2014
Building energy system retrofit and retro-commissioning projects present tremendous opportunities... more Building energy system retrofit and retro-commissioning projects present tremendous opportunities to save energy. Energy consumption in buildings, especially HVAC systems, is significantly impacted by weather conditions. However, short-or long-term climatic data are frequently missing because of data transmission problems, data quality assurance methods, sensor malfunction, or a host of other reasons. These gaps in climatic data continue to provide challenges for HVAC engineers in monitoring and verifying building energy performance. This article examines eight classical approaches that use Linear interpolation, Lagrange interpolation, and Cubic Spline interpolation techniques, and eleven approaches that use two newly developed methods, i.e., Angle-based interpolation and Corr-based interpolation, to restore up to 24 h of missing dry-bulb temperature data in a time series for use in building performance monitoring and analysis. Eleven one-year hourly data sets are used to evaluate the performance of these 19 different methods. Each method is applied to deal with artificial gaps that are generated randomly. In terms of the difference between estimated values and measured values, two types of comparisons are carried out. The first comparison is conducted with three evaluation indices: MAE, RMSE, and STDBIAS. The second comparison is based on the percentage of the total data that can be estimated by an approach within specific error thresholds, including 1 • F (0.56 • C), 2 • F (1.11 • C), 3 • F (1.67 • C), and 5 • F (2.78 • C), from measured values. The comparison results show that Linear interpolation performs best when filling 1-2 h gaps, Lagrange interpolation (Lag2L2R) outperforms other methods when gaps are 3-8 h long, and the Corr-based interpolation method (Corr1L1R24Avg) is a better technique for filling 9-24 h gaps. This article presents the first part of the research results through the ASHRAE 1413 research project. The second part of the results focuses on methods to filling long-term dry-bulb temperature gaps.
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Papers by Oluwaseyi Ogunsola
This study also proves that the analytical solution method is symptotically stable regardless of time step. A typical office was used as a case study and the predicted building loads are compared with measured data and numerical results from EnergyPlus. For the case study, the model demonstrated better accuracy and is seen to be robust for thermal load estimation for cooling season.
This study also proves that the analytical solution method is symptotically stable regardless of time step. A typical office was used as a case study and the predicted building loads are compared with measured data and numerical results from EnergyPlus. For the case study, the model demonstrated better accuracy and is seen to be robust for thermal load estimation for cooling season.