The Application of Rivet, HAP, and Ecotect Programs for the Analysis of Building Envelope Parameters to Optimize Energy Efficiency and Energy Savings: A Case of Baghdad-Iraq

Energy Conservation Strategy Research for Residential Building Refurbishment in Urban area of China

Energy consumption and efficiency technology measures in European non-residential buildings

Theoretical Minimum Thermal Load in Buildings

Energy is very important in social development and scientific and technological progress, which guarantees the transformation of the human renewal era. However, the energy on the earth is fixed and will be exhausted one day. Therefore, the research on energy conservation and related technologies in the field of architecture has become the focus of domestic and foreign scholars. Affected by factors such as personnel distribution, weather conditions and equipment operation time, BE consumption data is highly uncertain and random, and it is difficult to accurately predict BE consumption. Based on this, the purpose of this paper is to study different algorithms based on the comprehensive prediction model of building energy (BE) consumption. Firstly, this paper summarizes the energy consumption in the building field by consulting a large number of documents, and simulates the hourly and monthly energy consumption of the building based on EnergyPlus software. The monthly energy consumption is characterized by periodic oscillation, and a combined forecasting model of cumulative TGM-RBF is proposed. The experiment shows that the accuracy of the model in predicting the monthly BE consumption is 1.81% and 3.30% higher than that of the cumulative TGM (1,1) model (T-M) and GM (1,2) model respectively. Compared with G-M and cumulative TGM (1,2) model, the prediction accuracy of energy consumption is increased by 1.11% and 1.53% respectively.

A comparative study on optimum insulation thickness of walls and energy savings in equatorial and tropical climate

Improving building energy efficiency is a major goal of energy policies. In recent years, various energy efficiency projects have been underway to reduce greenhouse gases. The general method of evaluating energy savings focuses on performance analysis of energy saving technologies. However, fluctuations in energy consumption of buildings can be caused by issues other than changes in specific energy saving facilities. Therefore, it is cost-effective to determine energy savings through changes in the overall building energy consumption before and after implementation of ECM (Energy Conservation Measures). Through this, we statistically verified a method of considering outdoor temperatures when calculating the energy savings using the energy efficiency measurement method IPMVP (International Performance Measurement and Verification Protocol). Based on this analysis, unlike methods that assume numerous data will be highly correlated, we found that our model considering the data resolutions of the average daily temperature and daily power consumption was the most suitable and efficient among the models with various data resolutions. This shows that statistically appropriate results can be obtained even if there is only small amounts of outdoor temperature and daily energy consumption data needed for energy savings assessment. It is expected that energy management methodologies for practitioners will improve if analysis methods considering energy consumption patterns of buildings are further developed.

Analysis of national and local energy-efficiency design standards in the public building sector in China

Under the requirement of energy savings and emission reduction in China, building energy consumption, which occupies a rising proportion of the total energy consumption in society, has become the focus of energy conservation research. Public buildings with a high‐energy consumption level have become the most important part of energy conservation research. It is of great practical significance and social value to study energy conservation in large public buildings. In this paper, a large office building is taken as an example. First, a simulation model is constructed by using the energy consumption simulation analysis method, and the reliability of the model is verified by a comparison with the actual energy consumption. Second, based on the model, the thermal design parameters of six building envelope structures, including the external wall heat transfer coefficient, are analyzed in the order of energy‐saving sensitivity. Based on the results of the sensitivity analysis, suggestions for each factor are presented. Finally, considering the mutual influence of each parameter on the building energy consumption, the orthogonal design method is used to arrange the test, and the optimal scheme combination of the energy‐saving effect is analyzed, which can provide decision support for the energy saving of public building envelopes.

With the advancement of China's urbanization process, the amount of building energy consumption is gradually increasing every year, building energy consumption accounts for about one-third of the annual total energy consumption, the total amount is huge and there is a lot of energy waste, so the energy conservation of building energy consumption has become an important part of the "energy conservation and emission reduction" work. Before making plans for building energy conservation, the relevant data of energy consumption within the whole system need to be obtained first, so the building energy consumption monitoring system arises at the historic moment. In this paper, the overall design of building energy consumption monitoring system for stadiums and gymnasiums is based on data acquisition technology. The building energy consumption monitoring system required by this project mainly includes comprehensive collection of electricity and water consumption data of the whole stadium, classified statistics of the collected energy consumption data of each floor, and output various daily, monthly and annual reports. Finally, through the performance test of the system, the feasibility of the system is verified to meet the needs of users on the system.

The energy consumption of a residential building is mostly attended due to energy use and efficiency. The rate of building's energy consumption in developed countries is about one third of the total amount of energy consumption and this amount of energy consumption in developing countries is about 40 percent of the total amount of energy consumption. Accordingly, forecasting the energy consumption of buildings has been raised as a challenge in recent decades. Modeling energy consumption in residential buildings has become possible with the advances made in computing and simulation, and one of these significant advances is the emergence of artificial intelligence in the development of statistical models. Studies have shown that the Artificial Neural Network method can be used to predict the nonlinear behavior of building's energy consumption. In this model, climatic variables are the input and building energy consumption is the output variable. The network was built in MATLAB software and trained with Levenberg-Marquardt algorithm. The results show that the neural network has a 93.7 percent ability to estimate the energy consumption of buildings.

Energy consumption in buildings accounts for a notable part of the primary energy consumption all over the world. The building industry also has a great potential to decrease the environmental impact by reducing greenhouse gas emissions. The national strategies of many developing countries are shaped by energy conservation issues. Improving energy efficiency and productivity is stated as one of the main elements of the Turkish national energy strategy. An efficient way to decrease energy consumption in buildings is to implement insulation on the building envelope. Identifying the optimum insulation thickness to be applied on the exterior walls is of prime importance. This study adapts a stochastic approach to determine optimum insulation thickness for 81 cities in Turkey. The stochastic approach, unlike the commonly used deterministic approach, incorporates the probabilistic nature of the process and presents the optimum insulation thickness as a probability distribution graph rather than a single value. For this purpose, a number of insulation thicknesses (1-20 cm) were regarded as the alternatives and the optimum alternative was determined based on life cycle costing analysis involving the cost of insulation application and annual energy savings. The average monthly temperature of each city and financial parameters such as the inflation and discount rates were considered as the stochastic elements. The results of the life cycle costing analysis were used to (i) identify the optimum thicknesses in each city as a probability distribution graph and (ii) generate an optimum insulation thickness map for Turkey.

Determination of insulation thickness by means of exergy analysis in pipe insulation

Since China put forward the "double carbon" goal, reducing the energy consumption level of urban buildings has become an important component of energy conservation and emission reduction work. To realize the effective control of the energy consumption level of urban buildings and carry out accurate building energy consumption is the key foundation. However, at present, from the perspective of China's provinces, there is still a lack of authoritative relevant statistical data release and calculation. Based on this, this study takes Shandong Province of China as an example, explores the calculation method of building energy consumption based on energy balance table, and calculates and analyzes the corresponding urban building energy consumption, and obtains the urban building energy consumption of Shandong Province of China during 2012-2022. The analysis results show that the calculation method of building energy consumption based on energy balance table is suitable for the calculation of building energy consumption in Shandong Province, and it clearly describes the overall energy consumption, the proportion of energy consumption of various types of buildings and the energy consumption per unit area of buildings, and can provide a useful reference for the formulation of relevant building energy conservation and emission reduction policies.

Chinese solar greenhouses (CSGs) are characterized by unique walls to reduce the transmission of heat and promote the energy conservation in winter production, which promotes cultivation in the northeast region of China in winter. Effective selection of insulation material is important for the CSG based on the energy consumption and economic analysis. However, choosing the thickness of the insulation material in walls often discussed with the structure of CSG. There is a lack of research combing the optimal insulation thickness for improving the energy conservation. The aim of this study was to find the optimum insulation thickness during the energy conservation based on the structure of walls, the energy consumption in local climatic conditions, the cost of insulation material, and economic payback period over a lifetime. By the economic analysis of insulation thickness, thermal resistance, lifetime energy saving, and payback period, three kinds of typical walls (clay brick (CB), hollow concrete block (HCB) and fly ash block (FAB)) combed with four insulation materials including the expanded polystyrene, the foamed PVC, the perlite, and the rock wool were calculated. The optimum insulation thickness can be found when energy savings reached the maximum. In the northeast region, the association of FAB with rock wool as the insulation layer was the most economic composite wall structure. The optimum insulation thickness was 0.05 m, with the cost only 5 USD/m2. The thermal resistance of composite wall had a significant effect on the payback period. When thermal resistance increased from 0.2 to 1.2 m2K/W, the payback period varied from 0.4 to 4.3 years. What is more, the energy consumption in local climatic conditions had a more significant effect on payback period. It can be assumed that insulation materials are more favored in cold climatic regions where heating degree-days over 1600 °C days for payback periods is less than 2 years. These results have strong practical and economical significance in saving energy and improving the environment of CSG.

Research on the building energy efficiency design strategy of Chinese universities based on green performance analysis