Comparison of Building Simulation Methods for Modeling Apartment Balconies

The purpose of this study is to compare the load calculation results by a model using the air changes per hour (ACH) method and a model using an airflow network (AFN) and to ascertain what causes the difference between the two models. In the basic case study, the difference in the heat transfer distribution of the model in the interior space was investigated. The most significant difference between the two models is the heat transfer that results from infiltration. Parameter analysis was performed to investigate the relationship between the difference and the environmental variables. The result shows that the greater the difference is between the air temperature inside the balcony and the outdoor air temperature, and the greater the air flows from the balcony to the residential area, and the greater the heating and cooling load difference occurs. The analysis using the actual weather files of five domestic cities in South Korea rather than a virtual case shows that the differences are not so obvious when the wind blows at a constant speed throughout the year, but are dominant when the wind does not blow during the night and is stronger alongside the occurrence of sunlight during the day.

Prediction of Building Energy Consumption Based on IPSO-CLSTM Neural Network

Accurate prediction of building load is essential for energy saving and environmental protection. Exploring the impact of building characteristics on heating and cooling load can improve energy efficiency from the design stage of the building. In this paper, a prediction model of building heating and cooling loads is proposed, which based on Improved Particle Swarm Optimization (IPSO) algorithm and Convolution Long Short-Term Memory (CLSTM) neural network model. Firstly, the characteristic variables are extracted and evaluated by Spearman’s correlation coefficient method; Then the prediction model based on the CLSTM neural network is constructed to predict building heating and cooling load. The IPSO algorithm is adopted to solve the problem that manual work cannot precisely adjust parameters. In this method, the optimization ability of the PSO algorithm is improved by changing the updating rule of inertia weight and learning factors. Finally, the parameters of the neural network are taken as IPSO optimization object to improve the prediction accuracy. In the experimental stage of this paper, a variety of algorithm models are compared, and the results show that IPSO-CLSTM can get the best results in the prediction of heating and cooling load.

Development and Performance of Roof-Based Building-Integrated Photovoltaic-Thermal Systems: A Review

A building-integrated photovoltaic-thermal (BIPVT) system integrates building envelope and photovoltaic-thermal collectors to produce electricity and heat. In this paper, the electrical and thermal performance of roof-based BIPVT systems developed in the recent two decades and their effects on heating and cooling load of the building are reviewed. According to the use of thermal energy from the photovoltaic (PV) panels, the roof-based BIPVT are classified into three classes: cooling of PV, air heating, and water heating. Each class is further divided into several types according to the designs of the integrated PV roofs. Compared with BIPV systems, the total efficiency of most BIPVT systems is significantly improved. However, the decrease in electricity output and adverse impact on the indoor environment is also found for some designs of BIPVT systems in some climates. The advantages and disadvantages of various designs are discussed. Issues to be further studied in the future are also provided in this review.

Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

The Potential of Utilizing Buildings’ Foundations as Thermal Energy Storage (TES) Units from Solar Plate Collectors

Underfloor heating systems provide comfort due to the natural heat flow distribution by a network of pipes, conventionally connected to a heat pump operating at low temperatures. To this extent, a renewable energy source could be an alternative solution. Acting as a case to investigate such systems, the Mediterranean island of Cyprus with a plethora of sunny days points to solar energy as the obvious solution. In this study, solar collector systems are recruited to supply the required heat for a typical Cypriot house, with the building’s foundation acting as a thermal energy system (TES) unit. The heat supply to the building can then be distributed with natural convection from the TES. The solar collectors and the building’s foundation system are studied with the aid of two software programs, namely TRNSYS and COMSOL Multiphysics. The former is used for the calculation of the heating and cooling load of the house as well as to estimate the energy provided by the flat plate solar collectors at specific conditions. The latter is then used to examine the TES unit with the heat gain/loss of the building. The obtained results, including analyses on the solar collectors’ area and the foundation thickness indicate that the suggested system would be able to sufficiently cover, partially or fully, the building’s heating load.