Environmental concerns and growing energy costs raise the importance of sustainable development and energy conservation. The building sector accounts for a significant portion of total energy consumption. Passive cooling techniques provide a promising and cost-efficient solution to reducing the energy demand of buildings. Based on a typical residential case in Hong Kong, this study aims to analyze the integration of various passive cooling techniques on annual and hourly building energy demand with whole building simulation. The results indicate that infiltration and insulation improvement are effective in regard to energy conservation in buildings, while the effectiveness of variations in building orientation, increasing natural ventilation rate, and phase change materials (PCM) are less significant. The findings will be helpful in the passive house standard development in Hong Kong and contribute to the further optimization work to realize both energy efficiency and favorably built environments in residential buildings.

Sensors for data collecting are vital in the development of IoT and intelligent systems. High power consuming current and voltage monitors are indispensable in conducting maximum power point tracking (MPPT) in traditional PV energy wireless sensor nodes. This paper presents a sensor node system based on Neural Network MPPT with cloud method (NNwC) which utilizes information sharing process that is specific to sensor networks. NNwC uses a few sample sensor nodes to collect environmental parameter data such as light intensity (L) and temperature (T) to build the MPPT regression model by Neural Network. Then all other functional sensor nodes implement the model with their environmental parametervalues to conduct MPPT. As a result, the new sensor node system reduces energy consumption as well as the size and cost of the harvester. Then, this paper provides a SPICE simulation to estimate the percentage of power consumption reduced in the new sensor node system and also estimates the percentage of loss in neural network MPPT power generation compared with the perfect MPPT. Finally, the study compares the economic and environmental performance of the proposed system and the traditional ones through a case in a real building situation.Energies 2019, 12, 101 2 of 20 of pervasive and massive. Another solution employs the self-energized PV wireless sensor nodes [1,2] for power generation from solar energy. These systems commonly use batteries or supercapacitors for energy storage to maintain the continuous operation of sensor nodes. However, due to the limited PV conversion efficiency, this design requires large-size PV panels to generate enough energy for sensor nodes. These large-size sensor nodes will not only alter original environmental factors but also restrict their distribution range, and then, reduce the reliability of data collected by the sensors.Maximum power point tracking (MPPT) algorithms, a technique for increasing the output of the solar panel in a given size, is a promising solution to the energy storage problem in sensor node systems. However, MPPT requires voltage and current monitors to acquire the real-time power output of the harvester. Sensor node is a kind of low energy consumption application. In order to acquire more precise voltage and current parameters, the monitor components insulated with original output circuit should be adopted because they can obtain the voltage and current data without affecting the original values. The Hall Effect-Based Current/Voltage Monitors such as CSM series and VSM series are suitable for high accuracy current and voltage data collecting. However, the monitors, especially current monitors, not only have a high initial cost [3] but also require relatively large power support according to the load [4]. As the result, these sensors are commonly designed with space-consuming solar panels to avoid the interruption of sensors. To address such problem, there is a strong demand for a new wireless solar energy harvester for sensor node network systems with a higher conversi...