Abstract:This study has investigated the feasibility of three different solar-assisted air conditioning systems for typical medium-sized office buildings in all eight Australian capital cities using the whole building energy simulation software EnergyPlus. The studied solar cooling systems include: solar desiccant-evaporative cooling (SDEC) system, hybrid solar desiccant-compression cooling (SDCC) system, and solar absorption cooling (SAC) system. A referenced conventional vapor compression variable-air-volume (VAV) system has also been investigated for comparison purpose. The technical, environmental, and economic performances of each solar cooling system have been evaluated in terms of solar fraction (SF), system coefficient of performance (COP), annual HVAC (heating, ventilation, and air conditioning) electricity consumption, annual CO 2 emissions reduction, payback period (PBP), and net present value (NPV). The results demonstrate that the SDEC system consumes the least energy in Brisbane and Darwin, achieving 56.9% and 82.1% annual energy savings, respectively, compared to the conventional VAV system, while for the other six cities, the SAC system is the most energy efficient. However, from both energy and economic aspects, the SDEC system is more feasible in Adelaide, Brisbane, Darwin, Melbourne, Perth, and Sydney because of high annual SF and COP, low yearly energy consumption, short PBP and positive NPV, while for Canberra and Hobart, although the SAC system achieves considerable energy savings, it is not economically beneficial due to high initial cost. Therefore, the SDEC system is the most economically beneficial for most of Australian cities, especially in hot and humid climates. The SAC system is also energy efficient, but is not as economic as the SDEC system. However, for Canberra and Hobart, reducing initial cost is the key point to achieve economic feasibility of solar cooling applications.

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Solar desiccant cooling is widely considered as an attractive replacement for conventional vapor compression air conditioning systems because of its environmental friendliness and energy efficiency advantages. The system performance of solar desiccant cooling strongly depends on the input parameters associated with the system components, such as the solar collector, storage tank and backup heater, etc. In order to understand the implications of different design parameters on the system performance, this study has conducted a parametric analysis on the solar collector area, storage tank volume, and backup heater capacity of a solid solar desiccant cooling system for an office building in Brisbane, Australia climate. In addition, a parametric analysis on the outdoor air humidity ratio control set-point which triggers the operation of the desiccant wheel has also been investigated. The simulation results have shown that either increasing the storage tank volume or increasing solar collector area would result in both increased solar fraction (SF) and system coefficient of performance (COP), while at the same time reduce the backup heater energy consumption. However, the storage tank volume is more sensitive to the system performance than the collector area. From the economic aspect, a storage capacity of 30 m 3 /576 m 2 has the lowest life cycle cost (LCC) of $405,954 for the solar subsystem. In addition, 100 kW backup heater capacity is preferable for the satisfaction of the design regeneration heating coil hot water inlet temperature set-point with relatively low backup heater energy consumption. Moreover, an outdoor air humidity ratio control set-point of 0.008 kgWater/kgDryAir is more reasonable, as it could both guarantee the indoor design conditions and achieve low backup heater energy consumption.

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With the increase of household electricity consumption and the introduction of distributed new energy sources, more attention has been paid to the issue of optimizing the cost of electricity purchase for household customers. An effective way to deal with these problems is through home energy management system (HEMS). In this paper, a model of home energy management is presented to optimize the home energy mix. The operation of home electricity consumption devices, distributed generation systems, and energy storage devices, as well as the charging and discharging of electric vehicles, are all considered. HEMS is a self-regulating system that can accommodate fluctuations in tariffs and home electricity consumption. The structure and the optimal scheduling algorithm of HEMS are introduced. The smart grid and demand response, smart home, new energy generation, energy storage, and other related technologies are discussed. Furthermore, the optimal scheduling of power consumption devices and energy sources in the HEMS and future development directions are explained and analyzed. A framework of HEMS is presented on the basis of advanced metering infrastructure (AMI). The framework adopts a local information management terminal as the core of data storage and scheduling in the home. Based on the timely purchase of electricity from the grid and the generation of electricity in combination with PV systems, an optimized simulation model for the scheduling of a new home energy management system is established. In addition, the application prospects of artificial intelligence in the HEMS are overviewed.

Electricity theft detection is critical for the safe and effective development of the electric power system. The existing methods that are used to detect electricity theft rely on historical load data and are considered to have poor timeliness. Their detection results have limited reference to power supply enterprises’ investigation on electricity theft. Therefore, this paper proposes the Boruta-XGBoost power theft detection model based on multiple features of electric energy parameters. The model converts electricity theft detection problem into a multiclass classification problem. First, the features of various electric energy parameters that are collected by the electric energy metering device are used to construct the original dataset. Then, the Boruta algorithm is used to select features and reconstruct the dataset based on the selection results. Finally, the reconstructed dataset is used to train an XGBoost model that can detect the type of electricity theft based on the features of real-time electric energy parameters. In order to verify the effectiveness of the model, a comparative experiment is conducted in this paper. The reconstructed dataset is used to train the following algorithms: LightGBM, CART, SVM, and logistic regression models. The test results show that the Boruta-XGBoost model in this paper has the best effect on electricity theft detection.

The high energy use of hospitals and healthcare facilities globally contributes to greenhouse gas emissions. At the same time, a large percentage of this energy use is attributed to space heating, cooling and ventilation, and is hence correlated to the climate. While the energy performance of Australian hospitals at the design stage is evaluated using historical weather data, the impact of the warming climate on Australian hospitals into the future remains unknown. The research question addressed is: What is the impact of future climates on the energy use of Australian hospitals built with the current design conditions? Two archetype hospital models were developed (a small single-story healthcare facility and a large multi-story hospital). DesignBuilder was used to simulate the performance of these models in 10 locations, ranging from the tropics to cool temperate regions in Australia. Current (1990–2015) and future climate files (2030, 2050, 2070 and 2090) were used. The results show that with the warming climate, the heating demand decreased, while the cooling demand increased for both hospital models for all sites. Cooling dominated climates, such as Darwin and Brisbane, were significantly impacted by the changing climates due to a substantial increase in cooling energy use. Heating based climates, such as Hobart and Canberra, resulted in an overall small reduction in total building energy use. In addition, the single-story facility was more impacted by the change in climate (in terms of energy use intensity) than the multi-story facility. The study highlights the importance of future climate files in building simulation and decarbonization planning.

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