• Energy Research

Recovery of the industrial waste heat is a cost-effective and environmentally friendly approach for the energy supply in cities. The industrial waste heat supply chain is a typical multi-objective closed-loop system that aims at optimizing both the economic and environmental benefits through proper operations during supply, purchase, distribution, and consumption. Different from conventional waste heat recovery (WHR), the mobile waste heat recovery (M-WHR) implanted recovery vehicles to facility heat collection and distribution. As a new and cost-sensitive channel for the WHR industry, the dynamic supply chain of the M-WHR has seldom been studied. This study proposed a numerical model to investigate such a dynamic system and conducted a case study to verify the models. The results suggested that by optimizing the production plan based on the market demand, the supply chain can reach the Pareto state and minimize its production cost and idle production recourses. The results of the designed numerical examples verify the robustness of the operation strategy. Also, with proper government subsidies, the investment in constructing the WHR supply chain is profitable and the payback period can be reduced.

Heat storage phase change materials (PCM) has been widely used in various thermal energy systems, such as solar-heat pump. Harvesting solar energy requires low-cost and efficient PCM with proper melting points and high energy absorbing capacity. This paper proposed a novel Sodium acetate trihydrate (SAT) -Acetamide (AC) -micron/nano Aluminum nitride (AlN) composite phase change material (CPCM) with a melting point of 47.3 °C for solar-heat pump implementation. 10 wt% AC was selected as modifier to lower the melting point of SAT. The nano AlN or micron AlN was added to CPCM to suppress “supercooling” and improve its thermal conductivity. In order to determine the optimal ratio of mixture and examine the composite's thermal properties, DSC, SEM, XRD, FTIR and other characterization methods were implemented in this study. The results showed that 3 μm and 30 nm mixed AlN were the most effective nucleating agent and thermal conductivity filler. Also, the optimal composite with 5 wt% mixed AlN could reduce the “supercooling” degree to 0.27 °C and improve the thermal conductivity to 0.6484 W/m·K. After 100 heat absorbing and releasing cycles, the latent heat of CPCM maintained a respectable value of 226.8 kJ/kg.

Abstract As buildings consume substantial amounts of energy, researchers and decision makers are giving more attention to urban-scale energy assessment and design. In fact, researchers have developed many building-energy modeling and simulation tools that are regarded as effective for building designers and facility managers. However, few models have been found suitable for urban-scale efficient building design and planning. Indeed, to carry out urban-scale energy modeling and simulation, it is necessary to possess a comprehensive understanding of interactions among building groups as well as huge computational resources. To develop a more efficient and reliable simulation model, this study proposes a parallel computational building-chain (PCBC) model. This PCBC model aims to simplify building interactions and implement efficient multi-thread computations. It can decompose large-scale building groups into inter-connected building units by defining the thermal and shading boundary conditions of buildings in a neighborhood. By coupling individual buildings’ simulated energy consumption, the urban energy dynamics can be reconstructed. To validate the proposed method, researchers examined a sample urban-building group with 410 buildings. Compared with the conventional integrated Whole City model, the proposed method achieved nearly the same outputs with reduced computation time. With an increase in the simulation scale, computational efficiency can be improved in the future.

Abstract Occupancy attracts an increasing attention in recent building energy efficiency research through enabling more sophisticated control strategies. With the development of latest indoor positing systems (IPS), the facility managers are able to detect the geospatial distribution of building occupants. Based on such information, this paper proposes a demand-driven control system for HVAC control in large spaces to reconcile occupants’ thermal comfort demand and energy consumption. Comparing to conventional temperature and CO2 based HVAC control systems, the new approach integrates the indoor positions of occupants and a demand-oriented and PID-based ventilation control mechanism. An computational fluid dynamic simulation is constructed to valid the proposed control system. Air supply flow rate and temperature distribution are captured for three sample cases that have even and uneven occupancy distributions in the simulation. By avoiding overcooling and unnecessary cooling, the proposed approach could save a significant amount of electrical consumption from HVAC operation.

Abstract Solar energy radiation during the midday could causes the internal temperature of building rising, thus resulting in a significant source of power consumption to run air conditioner devices for thermal comfort environment. A growing interest developed in phase change materials (PCMs) applied in building envelope owing to the prevailing energy challenges. A new type of shape-stabilized composite phase change material (CPCM) was developed by introducing a novel Na2HPO4·12H2O–K2HPO4·3H2O (DSP-PPDT) eutectic hydrated salt into super absorbent polymer (SAP), which was characterized by differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD), Scanning electron microscope (SEM). Results indicated that DSP-PPDT eutectic hydrated salts in various ratios all can be formed, among which DSP-PPDT eutectic hydrated salt with the mass fraction of PPDT at 25% had a melting temperature of 24.26 °C, making it suitable for building envelope. The results of cooling tests suggested that 2% of Na2SiO3·9H2O nucleating agent could reduce the supercooling degree of the eutectic hydrated salt to 0.79 °C. The modified eutectic hydrated salt could be stabilized in the network structure of SAP at a mass fraction of 12% through physical interaction without leakage, which melted at 24.13 °C with the enthalpy of 172.7 J/g and had enhanced thermal stability, good thermal reliability at 100 thermal cycles as well as low thermal conductivity of 0.474 W/(m·K). The good thermal performances of CPCM make it a promising candidate applied in building envelope.

Abstract As most urban residents spend an average 90% of their time in buildings, it is crucial for building residents to monitor and maintain a comfortable indoor environment and air quality. Controlling the indoor carbon dioxide (CO2) concentration level is the major objective of mechanical ventilation systems to ensure healthy indoor air quality (IAQ). Current methods utilize proportional–integral–derivative (PID) controllers and multi-zone equations to operate ventilation systems in large spaces based on ASHRAE Standard 62. However, this approach suggests to determine the fresh air supply rate primarily based on CO2 level without considering occupancy. This often results in insufficient fresh air or energy waste in unnecessary mechanical operations in unoccupied building zones. Therefore, this study proposes a ventilation strategy based on occupancy profiles that captured by a Wi-Fi probe enabled occupancy sensing system. Based on the detected occupancy profiles, the proposed ventilation strategy and other two conventional strategies were compared in a two-day experiment conducted a multi-zone space. The comparison results in the experiment show that the proposed approach could maintain a CO2 concertation level lower than 1000 ppm for 94% (weekday) and 80% (weekend day) of day time for all zones. At the same time, the proposed strategy saved about 44.26% (weekday) and 55.5% (weekend day) of ventilation energy consumption when compared to the fixed rate ventilation strategy and saved about 23.6% (weekday) and 46.1% (weekend day) of ventilation energy consumption when compared to the multi-zone equation strategy.

Abstract Reliable occupancy estimation is the key to balancing energy use and comfort as well as promoting energy efficiency in buildings. In this study, a novel occupancy estimation model based on CO2 concentration data was proposed. A wavelet denoising method was applied to remove the random noise of the CO2 sensory data, and then a multi-grained scanning cascade forests (GcForest) method was used to estimate the number of occupants. The GcForest model incorporated three different tree-based classifiers in each level, enabling the estimation performance to be enhanced by exploiting the complementarity among the different learning algorithms. To evaluate the effectiveness of the proposed model, in this study a validation experiment was conducted in a university lab office and its results were compared with the support vector machines (SVM), classification and regression trees (CART), and inhomogeneous hidden Markov (IHMM) algorithms. The experimental results show that the wavelet denoising method could filter the noise and preserve the data features of the CO2 concentration. Moreover, the proposed model could achieve higher estimation accuracy, lower mean absolute error, and higher detection accuracy of the occupant presence/absence. Additionally, this model could capture both the first arrival time and the last departure time. Since the maximum depths of the classifiers affected the GcForest model's performance, the results also show that a proper selection of the maximum depth combination could lead to a significant improvement of the model estimation accuracy.

Abstract Energy Saving Performance Contracts (ESPCs) are a business model that aims to promote building energy efficiency through retrofitting with minimal or zero upfront costs for owners. Many studies show that occupants tend to use more energy than expected after retrofits (referred as rebound effect), which results in underestimated retrofitting costs. However, end users’ energy-using behaviors and their relationship to the ESPCs decision-making process have seldom been studied. This study aims to propose such a behavior-based model to assist the contract decision-making among the major stakeholders in a building’s retrofit, including building owners, Energy Service Companies (ESCOs), and renters. The proposed model incorporates renters’ rebound effect and investigates the impact that major variables have on the rebound effect. To validate and evaluate the performance of the proposed model, a real retrofitting project in Maryland, United States, was examined. The results show that the rebound effect can significantly increase the payback period of ESPCs contracts by up to 4 years and the contract duration is significantly affected by renters’ risk attitudes. The proposed model and findings can help ESCOs and building owners predict more accurate energy saving amounts and design proper retrofitting contracts.