• Energy Research

In building performance simulation, occupant behaviour contributes to large uncertainties, which often lead to considerable discrepancies between actual energy consumption and simulation results. This chapter aims to extract occupant-behaviour related electricity load patterns using classical K-means clustering approach at the initial investigation stage. Smart-metering data from a case study in Shanghai, China, was used for the load pattern analysis. The electricity load patterns of occupants were examined on a daily/weekly/seasonal basis. According to their load patterns, occupants were categorized as (a) white-collar workers, (b) poor or older families and (c) rich or young families. The daily patterns indicated that electricity use was much more random and fluctuated over a wide range. Most households of the monitored communities consumed relatively-low electricity; the characteristic double peak with higher level of consumption in the morning and evening were only apparent in a relatively small subset of residents (mostly white-collar workers). The weekly analysis found that significant load shifting towards weekend days occurred in the poor or old family group. The electricity saving potential was greatest in the white-collar workers and the rich or young family groups. This study concludes with recommendations to stakeholders utilizing our load profiling results. The research provides a rare insight into the electricity-use-related occupant behaviours of Shanghai residents through the case study of two communities. The findings of the study are also presented in a meaningful way so that they can directly aid the decision-making of governments and other stakeholders interested in energy efficiency. The research results are also relevant to the building energy simulation community as they are derived from observations, and thus can have the potential to improve the efficiency and accuracy of numerical simulation results.

PDMS–MWCNTs/TiO2 microparticles made by microfluidics can achieve 85% removal efficiency of RhB pollutant in wastewater via synergetic treatment.

Liquid-vapor flow in porous media is studied in this article. To fulfill this goal, a double-distribution-function lattice Boltzmann (LB) model is proposed based on the separate-phase governing equations at the representative elementary volume (REV) scale. Importantly, besides the Darcy force and capillary force, which were commonly included in previous studies, the LB model in this article also considers the inertial force characterized by the Forchheimer term. This feature enables the model to offer an effective description of liquid-vapor flow in porous media at low, intermediate and even high flow rates. We validated the LB model by simulating a single-phase flow in porous media driven by a pressure difference and found its results are in good agreement with the available analytical solutions. We then applied the model to study water-vapor flow in a semi-infinite porous region bounded by an impermeable and heated wall. The numerical simulation reveals the flow and mass transfer characteristics under the compounding effects of inertial, Darcy and capillary forces. Through a comparison with the results given by the generalized Darcy’s law, our numerical results directly evidence that the inertial force is a dominating factor when a fluid passes through porous media at an intermediate or high flow rate.

Abstract Today, performance of lithium-ion battery is still limited by its operating temperature. The upper bound is capped by 50 ∘C. However, this limit is easily surpassed if batteries work at a high room temperature. In this article, sintered copper-powder heat pipe combining with water spray at its condensation section is designed to attack this issue−The former is sandwiched among batteries, removing battery heat by air convection under normal thermal conditions. When battery operation deteriorates at a room temperature beyond 40 ∘C, water spray functions for rapid heat dissipation by droplet evaporation. To assess effectiveness of this battery thermal management (BTM) design, discharge of lithium iron phosphate batteries at two large currents, I d = 12.5 A and 24 A, are performed at 40 ∘C. The cooling performance of the proposed BTM system is examined at different air speeds, air relative humidity, spraying frequencies and duty cycles. It is also compared to other heat-pipe based BTM approaches, and tested in transient cycles. The results demonstrate the proposed BTM system is highly effective. In the case of I d = 24 A , the maximum temperature and maximum temperature difference of the battery surface are dropped by 29.2 ∘C and 8.0 ∘C in comparison to those without BTM aids. It well protects lithium-ion battery operating at a large discharging current in an adverse thermal environment.

The thermoelectric generator (TEG) as a noise-free, pollution-free and environmentally friendly energy-conversion device has been intensively investigated in the last few decades. For different TEG applications, the characteristics of their heat sources determine the structural designs of the TEG systems. In this work, we categorized TEG heat sources into three types: body heat, natural sources of heat energy, and industrial waste heat. The up-to-date three maim structural designs of wearable TEGs including film TEG, micro-bulk TEG, and yarn TEG were reviewed. For TEG using natural heat sources, the major structural designs of TEG using solar energy, geothermal energy, and ambient heat energy were summarized. In terms of TEG using industrial waste heat, the structures of TEG systems applied in automotive, household stoves, and factories were concluded. Meanwhile, key thermal enhancement technologies including different heat exchangers, heat pipes, and phase change material (PCM) to improve the performance of these TEG systems, as well as challenges of each TEG system were also concluded. This review is expected to provide inspiration for the structural designs of TEG systems with different types of heat sources and to offer potential performance enhancement technologies for engineers and researchers in this field.

Abstract A recent surge of interest in building energy consumption has generated a tremendous amount of energy data, which boosts the data-driven algorithms for broad application throughout the building industry. This article reviews the prevailing data-driven approaches used in building energy analysis under different archetypes and granularities, including those methods for prediction (artificial neural networks, support vector machines, statistical regression, decision tree and genetic algorithm) and those methods for classification (K-mean clustering, self-organizing map and hierarchy clustering). The review results demonstrate that the data-driven approaches have well addressed a large variety of building energy related applications, such as load forecasting and prediction, energy pattern profiling, regional energy-consumption mapping, benchmarking for building stocks, global retrofit strategies and guideline making etc. Significantly, this review refines a few key tasks for modification of the data-driven approaches in the context of application to building energy analysis. The conclusions drawn in this review could facilitate future micro-scale changes of energy use for a particular building through the appropriate retrofit and the inclusion of renewable energy technologies. It also paves an avenue to explore potential in macro-scale energy-reduction with consideration of customer demands. All these will be useful to establish a better long-term strategy for urban sustainability.