The continuous development and increasing availability of Internet of Things (IoT) solutions have led to an era of connectivity in which everyday objects—from household appliances to industrial machines—are connected via the Internet [...]
Nowadays, portable air conditioners (PACs) have gained a broad international market with their advantages of easy installation and flexibility. The water wheel is generally used to solve the drainage problem of the PACs in the high-humidity environment. The reasonable configuration of the water wheel can help extend the continuous operation time and improve the performance of the PACs. This study was built upon existing PACs, exploring the impact of water wheel configurations on the performance of PACs in high-humidity environments. The research revealed that among various water wheel configurations, a dual water wheel configuration functioned better than a single one to help solve drainage problem. And the water wheel configuration that sprays water to the highest temperature area of the condenser proved the most effective for condensate removal. The optimal drainage characteristics of this particular water wheel configuration corresponded to the highest Energy Efficiency Ratio (EER) observed in the PACs, with the maximum increase of 21.11% compared to other water wheel configurations. Additionally, Due to the effect of condensate, with an increase in relative humidity, there was a simultaneous rise in the EER for all water wheel configurations in high-humidity environments.
This paper reviews the stability, heat transfer efficiency and photothermal conversion efficiency optimization studies of solid-liquid phase change materials (PCM) applied to water heaters. Suggestions and prospects were proposed. The study shows that the solid-liquid PCM are mostly filled in the water tank, thermal storage devices or solar thermal collector heater system with paraffin wax, and the addition of solid-liquid PCM can significantly improve the water heater performance. Further in-depth research is needed on PCM dosage and system economics of their application in heat pump water heaters, and the application of photothermal conversion PCM in solar water heater systems.
The advances in the Internet of Things (IoT) and cloud computing opened new opportunities for developing various smart grid applications and services. The rapidly increasing adoption of IoT devices has enabled the development of applications and solutions to manage energy consumption efficiently. This work presents the design and implementation of a home energy management system (HEMS), which allows collecting and storing energy consumption data from appliances and the main load of the home. Two scenarios are designed and implemented: a local HEMS isolated from the Internet and relies on its processing and storage duties using an edge device and a Cloud HEMS using AWS IoT Core to manage incoming data messages and provide data-driven services and applications. A testbed was carried out in a real house in the city of Valparaiso, Chile, over a one-year period, where four appliances were used to collect energy consumption using smart plugs, as well as collecting the main energy load of the house through a data logger acting as a smart meter. To the best of our knowledge, this is the first electrical energy dataset with a 10-second sampling rate from a real household in Valparaiso, Chile. Results show that both implementations perform the baseline tasks (collecting, storing, and controlling) for a HEMS. This work contributes by providing a detailed technical implementation of HEMS that enables researchers and engineers to develop and implement HEMS solutions to support different smart home applications.
Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appliance level without needing a smart meter plugged in to each load. However, the use of public datasets with pre-collected data employed for energy disaggregation is associated with limitations regarding its compatibility with random households, while gathering data on the ground still requires extensive, and hitherto under-deployed, equipment and time commitments. Going beyond these two approaches, here, we propose a novel data acquisition protocol based on multiplexing appliances’ signals to create an artificial database for energy disaggregation implementations tailored to each household and dedicated to performing under conditions of time and equipment constraints, requiring that only one smart meter be used and for less than a day. In a case study of a Greek household, we train and compare four common algorithms based on the data gathered through this protocol and perform two tests: an out-of-sample test in the artificially multiplexed signal, and an external test to predict the household’s appliances’ operation based on the time series of a real total consumption signal. We find accurate monitoring of the operation and the power consumption level of high-power appliances, while in low-power appliances the operation is still found to be followed accurately but is also associated with some incorrect triggers. These insights attest to the efficacy of the protocol and its ability to produce meaningful tips for changing energy behaviours even under constraints, while in said conditions, we also find that long short-term memory neural networks consistently outperform all other algorithms, with decision trees closely following.
