The Optimum Thickness of Rockwool as Roof Thermal Insulation: An Experimental and Numerical Study

Now a days, the global warming has increased the temperature in the environment that forced the building occupant to get assisting from the air condition to reduce the heat tension inside the building, this could increase the electricity bill amount. The aim of this study is to measure the optimum thickness of Rockwool insulation to experimentally and numerically to reduce the heating load inside the buildings. Two devices have been used through this research, Infrared Thermometer to measure profile temperature of the walls along with VELOCICALC to measure the air temperature and air velocity. Three different layers of Rockwool insulation have been applied on the roof of wooden room. The data present the two layers thickness of Rockwool is the best selection to reduce the heating load inside the room, the differential between outside and inside is 0.9 °C, the Rockwool of one layer reduced only 0.5 °C and the maximum thickness with three layers reduced only 1 °C, which is not much effective compared to the two layers but even more costly. CFD analysis shows agreement with the experimental result. The results shows if the dimensions of a UCSI lecture room is to be considered, then applying Rockwool insulation with a thickness of 100 mm would cost around RM 1520 as a UCSI lecture room is of 8 m width and 9 m length. However, two layers of Rock wool insulation could save around 29.30% of ROI per annum.

An Assessment of Heating Load Reduction by a Solar Air Heater in a Residential Passive Ventilation System

In this paper, the authors examined the technology to maximize the use of renewable energy. Passive ventilation systems are expected to reduce the energy consumption of the fan and the maintenance burden. In addition, the wall-mounted solar air heater can supply thermal energy without using any energy at all. Therefore, this paper presents a “passive ventilation system with a solar air heater” that combines a passive ventilation system with the solar air heater to preheat the air. This system can reduce the ventilation load. To evaluate the solar air heater performance in a real environment, we developed a simulation for calculating the heat collection capacity of the solar air heater, and then the system was implemented in a real building for verification. The simulation performs hourly unsteady calculations, allowing for accurate evaluation of the annual simulation. Based on the measurement results, the effects of heating load reduction and prediction methods are presented. The solar air heater reduced the monthly ventilation load by up to 50% or more, and by at least 15%. It was also confirmed that there was a high correlation between the actual measurements and the simulation results.

An Assessment of Heating Load Reduction by Solar Air Heater in Residential Passive Ventilation System

This paper, we examined the technology to maximize the use of renewable energy. The passive ventilation system is expected to reduce the energy consumption of the fan power and the maintenance burden. In addition, the wall-mounted solar air heater can supply thermal energy without using any energy at all. Therefore, we propose a "passive ventilation system with a solar air heater" that combines a passive ventilation system with solar air heater to preheat the air supply and reduce the ventilation load. To evaluate the solar air heater performance in a real environment, we developed a simulation for calculating the heat collection capacity of the solar air heater, and then implemented the system in a real building for verification. Based on the measurement results, the effects of heating load reduction and prediction methods are presented.

Heating load prediction based on particle swarm optimization support vector machine

Heating load is affected by many uncertain factors, which makes it show certain randomness. To further improve the heating load forecasting accuracy, reduce the prediction error, using cross validation (CV) ideology in the choice of a model of performance evaluation and the superiority, combined with the advantages of particle swarm optimization (PSO), which is easy to implement and has stronger global optimization ability, the important parameters (penalty factor C and RBF kernel function parameter γ) are optimized, and the best parameters are automatically found in the training set, so as to obtain the best training model. Compared with other algorithms, the model precision of this method is improved a lot, and the prediction result is more accurate.

Information model development for the quality assurance of technical equipment in small buildings

Ensuring energy quality, i.e. compliance with specified or standardised energy performance indicators of buildings over the entire life cycle, can reduce the climate impact of the building sector. In this work, the causes for quality deficiencies in small building services installations as well as the structure of actors interacting with building services were analysed. On this basis, we developed a solution approach in the form of an information model including a catalogue of all requirements and the necessary content to address and improve the quality assurance of HVAC in small buildings. This information model will serve as the basis for future digital tools that can perform (partially) automated heating load calculations and the design of heating systems, improve installation logistics and installation quality through providing information and the necessary processing, and enable improved hydraulic balancing and control.

Roof Color-Based Warm Roof Evaluation in Cold Regions Using a UAV Mounted Thermal Infrared Imaging Camera

Existing studies on reducing urban heat island phenomenon and building temperature have been actively conducted; however, studies on investigating the warm roof phenomenon to in-crease the temperature of buildings are insufficient. A cool roof is required in a high-temperature region, while a warm roof is needed in a low-temperature or cold region. Therefore, a warm roof evaluation was conducted in this study using the roof color (black, blue, green, gray, and white), which is relatively easier to install and maintain compared to conventional insulation materials and double walls. A remote sensing method via an unmanned aerial vehicle (UAV)-mounted thermal infrared (TIR) camera was employed. For warm roof evaluation, the accuracy of the TIR camera was verified by comparing it with a laser thermometer, and the correlation between the surface temperature and the room temperature was also confirmed using Pearson correlation. The results showed significant surface temperature differences ranging from 8 °C to 28 °C between the black-colored roof and the other colored roofs and indoor temperature differences from 1 °C to 7 °C. Through this study, it was possible to know the most effective color for a warm roof according to the color differences. This study gave us an idea of which color would work best for a warm roof, as well as the temperature differences from other colors. We believe that the results of this study will be helpful in heating load research, providing an objective basis for determining whether a warm roof is applied.