Zeyu Liu

The heat pipe (HP) is widely applied in the thermal management field at present. In order to make use of the low-grade and renewable energies to maintain building thermal comfort in the heating season, more and more studies with respect to improving the thermal performance of the building heating system integrated with the HP (BHSIHP), such as the floor heating system integrated with the HP (FHSIHP), the thermal storage wall heating system integrated with the HP (TSWIHP), conventional wall integrated with the HP (WIHP) and radiator heating system integrated with the HP (RHSIHP), are conducted. This paper aims to summarize different types of HPs applied in the building heating system and offers an overview of the thermal performance improvement for the BHSIHP. The thermal response, thermal conductivity, thermal resistance, heat capacity, heat transfer coefficient, temperature distribution, thermal storage and heat release capacity are always selected to investigate characteristics of the BHSIHP. Results show that the thermal performance of the FHSIHP, the TSWIHP, the WIHP and the RHSIHP is more outstanding than that of the conventional heating system. The thermal performance of the BHSIHP is affected by heat source temperature, installation tilt angle, working fluid, and filling ratio of the HP. The heat source temperature, which positively affects the performance of the BHSIHP, is crucial for the selection of the working fluid and filling ratio. However, the performance of the BHSIHP is increased first and then decreased with the increase of the installation tilt angle. The optimal filling ratio of the working fluid has been proven not to be a fixed value.

Artificial intelligence algorithms (AIAs) have gained widespread adoption in air conditioning load prediction. However, their prediction accuracy is substantially influenced by the quality of training samples. To improve the prediction accuracy of air conditioning load, this study presents an AIA prediction model based on the method of similarity sample screening. Initially, the comprehensive similarity coefficient between samples was obtained by using the gray correlation method improved with information entropy. Subsequently, a subset of closely related samples was extracted from the original dataset and employed to train the artificial intelligence prediction model. Finally, the trained AIA prediction model was used to predict the air conditioning load. The results illustrate that the method of similarity sample screening effectively improved the prediction accuracy of BP neural network (BPNN) and extreme learning machine (ELM) prediction models. However, it is essential to note that this approach may not be suitable for genetic algorithm BPNN (GABPNN) and support vector regression (SVR) models.

Artificial intelligence algorithms have gained widespread adoption in the field of air conditioning load prediction. However, their prediction accuracy is substantially influenced by the quality of training samples. Training samples that lack relevance to the predicted moments can introduce interference into the neural network's learning process, potentially leading to a state of local convergence during its iterative process. This, in turn, diminishes the robustness and generalization capabilities of the prediction model. To enhance the prediction accuracy of air conditioning load prediction models based on artificial intelligence algorithms, this study presents an artificial intelligence algorithm prediction model based on the method of sample similarity sample screening. Initially, the comprehensive similarity coefficient between samples is computed using the gray correlation analysis method, enriched with enhancements in information entropy. Subsequently, a subset of closely related samples is extracted from the original dataset and employed as the training dataset for the artificial intelligence prediction model. Finally, the trained artificial intelligence algorithm prediction model is deployed for air conditioning load prediction. The results illustrate that the method of screening training samples based on sample similarity effectively improves the prediction accuracy of BP neural network (BPNN) and extreme learning machine (ELM) prediction models. However, it is important to note that this approach may not be suitable for genetic algorithm BPNN (GABPNN) and support vector regression (SVR) models.

An air-conditioning system (ACS), which consumes large amounts of high-grade energy, is essential for maintaining the indoor thermal environment of modern buildings. However, an ACS consumes almost half of the total energy of the building. Therefore, it is necessary to reduce the energy consumption of the ACS to promote energy conservation and emission reduction in the building sector. In fact, there is an abundance of waste heat and low-grade energies with the potential to be utilized in ACS in nature, but many of them are not utilized efficiently or cannot be utilized at all due to the low efficiency of thermal energy conversion. Known as a passive thermal transfer device, the application of a heat pipe (HP) in the ACS has shown explosive growth in recent years. HPs have been demonstrated to be an effective method for reducing building cooling and heating demands and energy consumption in ACS with experimental and simulation methods. This paper summarizes the different HP types applied in the ACS and provides brief insight into the performance enhancement of the ACS integrated with HP. Four types of HPs, namely tubular HP (THP), loop HP (LHP), pulsating HP (PHP) and flat HP (FHP), are presented. Their working principles and scope of applications are reviewed. Then, HPs used in natural cooling system, split air conditioner (SAC), centralized ACS (CACS) and cooling terminal devices are comprehensively reviewed. Finally, the heat transfer characteristics and energy savings of the above systems are critically analyzed. The results show that the performance of the HP is greatly affected by its own structure, working fluid and external environmental conditions. The energy saving of ACS coupled with HP is 3–40.9%. The payback period of this system ranges from 1.9–10 years. It demonstrates that the HP plays a significant role in reducing ACS energy consumption and improving indoor thermal comfort.