A short- and medium-term forecasting model for roof PV systems with data pre-processing.

This study worked with Chunghwa Telecom to collect data from 17 rooftop solar photovoltaic plants installed on top of office buildings, warehouses, and computer rooms in northern, central and southern Taiwan from January 2021 to June 2023. A data pre-processing method combining linear regression and K Nearest Neighbor (k-NN) was proposed to estimate missing values for weather and power generation data. Outliers were processed using historical data and parameters highly correlated with power generation volumes were used to train an artificial intelligence (AI) model. To verify the reliability of this data pre-processing method, this study developed multilayer perceptron (MLP) and long short-term memory (LSTM) models to make short-term and medium-term power generation forecasts for the 17 solar photovoltaic plants. Study results showed that the proposed data pre-processing method reduced normalized root mean square error (nRMSE) for short- and medium-term forecasts in the MLP model by 17.47% and 11.06%, respectively, and also reduced the nRMSE for short- and medium-term forecasts in the LSTM model by 20.20% and 8.03%, respectively.

An overview of commercialization and marketization of thermoelectric generators for low-temperature waste heat recovery.

According to statistics, low-temperature waste heat below 300°C accounts for more than 89% of industrial waste heat. If the waste heat is not recycled, a large amount of low-temperature waste heat will be released into the atmosphere, thereby exacerbating global warming and posing a significant threat to human survival. Although the power generation efficiency of solid-state thermoelectric generation technology is lower than the organic Rankine cycle, it only requires a smaller construction area, which increases its market acceptance, applicability, and penetration. Especially in the pursuit of net-zero emissions by global companies, the importance of low-temperature waste heat recovery and power generation is even more prominent. The current thermoelectric conversion efficiency of commercial thermoelectric chips is about 5%. Power generation cost, thermoelectric conversion efficiency, and energy use efficiency are highly correlated with the commercialization of solid-state thermoelectric technology. This research shares five practical waste heat power generation cases commercialized by recycling three heat sources. It also points out the three significant challenges facing the commercialization of power generation from low-temperature waste heat recovery. This study analyzes 2,365 TEG patents submitted by 28 companies worldwide to determine the basic technology for realizing waste heat recovery through TEG and explore the potential commercialization of related waste heat recovery products. The future challenge for the large-scale commercialization of solid-state thermoelectric technology is not technological development but financial incentives related to changes in international energy prices and subsidies that promote zero carbon emissions.

Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results.

The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, were used to build artificial intelligence (AI) assisted heating ventilation and air conditioning (HVAC) control models. The AI control system, in conjunction with a specifically designed prior information notice (PIN) sensor, was used to improve the prediction accuracy. This data collected between 2012 and 2016 was used for AI training and PIN sensor testing. During the hottest week of 2017 in Taiwan, the PIN sensor was used to conduct temperature and humidity data predictions. A model-based predictive control was developed to obtain air conditioning energy consumption data. The comparative results between the predictive and actual data showed that the temperature and humidity prediction accuracies were between 95.5 and 96.6%, respectively. Additionally, energy savings amounting to 39.8% were achieved compared to the theoretical estimates of 44.6%, a difference of less than 5%. These results show that the experimental model supports the theoretical estimations. In the future, a PIN sensor will be installed in a chiller to further verify the energy savings of the AI assisted HVAC control.

Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis.

In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.

Enabling Smart Air Conditioning by Sensor Development: A Review.

The study investigates the development of sensors, in particular the use of thermo-fluidic sensors and occupancy detectors, to achieve smart operation of air conditioning systems. Smart operation refers to the operation of air conditioners by the reinforcement of interaction to achieve both thermal comfort and energy efficiency. Sensors related to thermal comfort include those of temperature, humidity, and pressure and wind velocity anemometers. Improvements in their performance in the past years have been studied by a literature survey. Traditional occupancy detection using passive infra-red (PIR) sensors and novel methodologies using smartphones and wearable sensors are both discussed. Referring to the case studies summarized in this study, air conditioning energy savings are evaluated quantitatively. Results show that energy savings of air conditioners before 2000 was 11%, and 30% after 2000 by the integration of thermo-fluidic sensors and occupancy detectors. By utilizing wearable sensing to detect the human motions, metabolic rates and related information, the energy savings can reach up to 46.3% and keep the minimum change of predicted mean vote (∆PMV→0), which means there is no compromise in thermal comfort. This enables smart air conditioning to compensate for the large variations from person to person in terms of physiological and psychological satisfaction, and find an optimal temperature for everyone in a given space. However, this tendency should be evidenced by more experimental results in the future.

Novel real-time diagnosis of the freezing process using an ultrasonic transducer.

The freezing stage governs several critical parameters of the freeze drying process and the quality of the resulting lyophilized products. This paper presents an integrated ultrasonic transducer (UT) in a stainless steel bottle and its application to real-time diagnostics of the water freezing process. The sensor was directly deposited onto the stainless steel bottle using a sol-gel spray technique. It could operate at temperature range from -100 to 400 °C and uses an ultrasonic pulse-echo technique. The progression of the freezing process, including water-in, freezing point and final phase change of water, were all clearly observed using ultrasound. The ultrasonic signals could indicate the three stages of the freezing process and evaluate the cooling and freezing periods under various processing conditions. The temperature was also adopted for evaluating the cooling and freezing periods. These periods increased with water volume and decreased with shelf temperature (i.e., speed of freezing). This study demonstrates the effectiveness of the ultrasonic sensor and technology for diagnosing and optimizing the process of water freezing to save energy.

Novel real-time temperature diagnosis of conventional hot-embossing process using an ultrasonic transducer.

This paper presents an integrated high temperature ultrasonic transducer (HTUT) on a sensor insert and its application for real-time diagnostics of the conventional hot embossing process to fabricate V-cut patterns. The sensor was directly deposited onto the sensor insert of the hot embossing mold by using a sol-gel spray technique. It could operate at temperatures higher than 400 °C and uses an ultrasonic pulse-echo technique. The ultrasonic velocity could indicate the three statuses of the hot embossing process and also evaluate the replication of V-cut patterns on a plastic plate under various processing conditions. The progression of the process, including mold closure, plastic plate softening, cooling and plate detachment inside the mold, was clearly observed using ultrasound. For an ultrasonic velocity range from 2197.4 to 2435.9 m/s, the height of the V-cut pattern decreased from 23.0 to 3.2 µm linearly, with a ratio of -0.078 µm/(m/s). The incompleteness of the replication of the V-cut patterns could be indirectly observed by the ultrasonic signals. This study demonstrates the effectiveness of the ultrasonic sensors and technology for diagnosing the replicating condition of microstructures during the conventional hot embossing process.

Smart sensors enable smart air conditioning control.

In this study, mobile phones, wearable devices, temperature and human motion detectors are integrated as smart sensors for enabling smart air conditioning control. Smart sensors obtain feedback, especially occupants' information, from mobile phones and wearable devices placed on human body. The information can be used to adjust air conditioners in advance according to humans' intentions, in so-called intention causing control. Experimental results show that the indoor temperature can be controlled accurately with errors of less than ±0.1 °C. Rapid cool down can be achieved within 2 min to the optimized indoor capacity after occupants enter a room. It's also noted that within two-hour operation the total compressor output of the smart air conditioner is 48.4% less than that of the one using On-Off control. The smart air conditioner with wearable devices could detect the human temperature and activity during sleep to determine the sleeping state and adjusting the sleeping function flexibly. The sleeping function optimized by the smart air conditioner with wearable devices could reduce the energy consumption up to 46.9% and keep the human health. The presented smart air conditioner could provide a comfortable environment and achieve the goals of energy conservation and environmental protection.

Investigating energy-saving potentials in the cloud.

Collecting webpage messages can serve as a sensor for investigating the energy-saving potential of buildings. Focusing on stores, a cloud sensor system is developed to collect data and determine their energy-saving potential. The owner of a store under investigation must register online, report the store address, area, and the customer ID number on the electric meter. The cloud sensor system automatically surveys the energy usage records by connecting to the power company website and calculating the energy use index (EUI) of the store. Other data includes the chain store check, company capital, location price, and the influence of weather conditions on the store; even the exposure frequency of store under investigation may impact the energy usage collected online. After collecting data from numerous stores, a multi-dimensional data array is constructed to determine energy-saving potential by identifying stores with similarity conditions. Similarity conditions refer to analyzed results that indicate that two stores have similar capital, business scale, weather conditions, and exposure frequency on web. Calculating the EUI difference or pure technical efficiency of stores, the energy-saving potential is determined. In this study, a real case study is performed. An 8-dimensional (8D) data array is constructed by surveying web data related to 67 stores. Then, this study investigated the savings potential of the 33 stores, using a site visit, and employed the cloud sensor system to determine the saving potential. The case study results show good agreement between the data obtained by the site visit and the cloud investigation, with errors within 4.17%. Among 33 the samples, eight stores have low saving potentials of less than 5%. The developed sensor on the cloud successfully identifies them as having low saving potential and avoids wasting money on the site visit.

Performance evaluation of UHF RFID technologies for real-time bus recognition in the Taipei Bus Station.

Transport stations such as airports, ports, and railways have adopted blocked-type pathway management to process and control travel systems in a one-directional manner. However, this excludes highway transportation where large buses have great variability and mobility; thus, an instant influx of numerous buses increases risks and complicates station management. Focusing on Taipei Bus Station, this study employed RFID technology to develop a system platform integrated with modern information technology that has numerous characteristics. This modern information technology comprised the following systems: ultra-high frequency (UHF) radio-frequency identification (RFID), ultrasound and license number identification, and backstage graphic controls. In conclusion, the system enabled management, bus companies, and passengers to experience the national bus station's new generation technology, which provides diverse information and synchronization functions. Furthermore, this technology reached a new milestone in the energy-saving and efficiency-increasing performance of Taiwan's buses.

A novel sensor platform matching the improved version of IPMVP option C for measuring energy savings.

It is easy to measure energy consumption with a power meter. However, energy savings cannot be directly computed by the powers measured using existing power meter technologies, since the power consumption only reflects parts of the real energy flows. The International Performance Measurement and Verification Protocol (IPMVP) was proposed by the Efficiency Valuation Organization (EVO) to quantify energy savings using four different methodologies of A, B, C and D. Although energy savings can be estimated following the IPMVP, there are limitations on its practical implementation. Moreover, the data processing methods of the four IPMVP alternatives use multiple sensors (thermometer, hygrometer, Occupant information) and power meter readings to simulate all facilities, in order to determine an energy usage benchmark and the energy savings. This study proposes a simple sensor platform to measure energy savings. Using usually the Electronic Product Code (EPC) global standard, an architecture framework for an information system is constructed that integrates sensors data, power meter readings and occupancy conditions. The proposed sensor platform is used to monitor a building with a newly built vertical garden system (VGS). A VGS shields solar radiation and saves on energy that would be expended on air-conditioning. With this platform, the amount of energy saved in the whole facility is measured and reported in real-time. The data are compared with those obtained from detailed measurement and verification (M&V) processes. The discrepancy is less than 1.565%. Using measurements from the proposed sensor platform, the energy savings for the entire facility are quantified, with a resolution of ±1.2%. The VGS gives an 8.483% daily electricity saving for the building. Thus, the results show that the simple sensor platform proposed by this study is more widely applicable than the four complicated IPMVP alternatives and the VGS is an effective tool in reducing the carbon footprint of a building.

A real-time convective PCR machine in a capillary tube instrumented with a CCD-based fluorometer.

This research reports the design, analysis, integration, and test of a prototype of a real-time convective polymerase chain reaction (RT-cPCR) machine that uses a color charged coupled device (CCD) for detecting the emission of fluorescence intensity from an RT-cPCR mix in a microliter volume glass capillary. Because of its simple mechanism, DNA amplification involves employing the cPCR technique with no need for thermocycling control. The flow pattern and temperature distribution can greatly affect the cPCR process in the capillary tube, a computational fluid dynamics (CFD) simulation was conducted in this study for the first time to estimate the required period of an RT-cPCR cycle. This study also tested the PCR mix containing hepatitis B virus (HBV) plasmid samples by using SYBR Green I fluorescence labeling dye to assess the prototype performance. The measured results from the image-processing scheme indicate that the RT-cPCR prototype with a CCD-based fluorometer can achieve similar DNA quantification reproducibility compared to commercial machines, even when the initial DNA concentration in the test PCR mix is reduced to 10 copies/µL.

The development of a monitoring system using a Wireless and Powerless Sensing Node deployed inside a spindle.

Installation of a Wireless and Powerless Sensing Node (WPSN) inside a spindle enables the direct transmission of monitoring signals through a metal case of a certain thickness instead of the traditional method of using connecting cables. Thus, the node can be conveniently installed inside motors to measure various operational parameters. This study extends this earlier finding by applying this advantage to the monitoring of spindle systems. After over 2 years of system observation and optimization, the system has been verified to be superior to traditional methods. The innovation of fault diagnosis in this study includes the unmatched assembly dimensions of the spindle system, the unbalanced system, and bearing damage. The results of the experiment demonstrate that the WPSN provides a desirable signal-to-noise ratio (SNR) in all three of the simulated faults, with the difference of SNR reaching a maximum of 8.6 dB. Following multiple repetitions of the three experiment types, 80% of the faults were diagnosed when the spindle revolved at 4,000 rpm, significantly higher than the 30% fault recognition rate of traditional methods. The experimental results of monitoring of the spindle production line indicated that monitoring using the WPSN encounters less interference from noise compared to that of traditional methods. Therefore, this study has successfully developed a prototype concept into a well-developed monitoring system, and the monitoring can be implemented in a spindle production line or real-time monitoring of machine tools.

A wireless sensor enabled by wireless power.

Through harvesting energy by wireless charging and delivering data by wireless communication, this study proposes the concept of a wireless sensor enabled by wireless power (WPWS) and reports the fabrication of a prototype for functional tests. One WPWS node consists of wireless power module and sensor module with different chip-type sensors. Its main feature is the dual antenna structure. Following RFID system architecture, a power harvesting antenna was designed to gather power from a standard reader working in the 915 MHz band. Referring to the Modbus protocol, the other wireless communication antenna was integrated on a node to send sensor data in parallel. The dual antenna structure integrates both the advantages of an RFID system and a wireless sensor. Using a standard UHF RFID reader, WPWS can be enabled in a distributed area with a diameter up to 4 m. Working status is similar to that of a passive tag, except that a tag can only be queried statically, while the WPWS can send dynamic data from the sensors. The function is the same as a wireless sensor node. Different WPWSs equipped with temperature and humidity, optical and airflow velocity sensors are tested in this study. All sensors can send back detection data within 8 s. The accuracy is within 8% deviation compared with laboratory equipment. A wireless sensor network enabled by wireless power should be a totally wireless sensor network using WPWS. However, distributed WPWSs only can form a star topology, the simplest topology for constructing a sensor network. Because of shielding effects, it is difficult to apply other complex topologies. Despite this limitation, WPWS still can be used to extend sensor network applications in hazardous environments. Further research is needed to improve WPWS to realize a totally wireless sensor network.

Investigation of electrical and magnetic properties of ferro-nanofluid on transformers.

This study investigated a simple model of transformers that have liquid magnetic cores with different concentrations of ferro-nanofluids. The simple model was built on a capillary by enamel-insulated wires and with ferro-nanofluid loaded in the capillary. The ferro-nanofluid was fabricated by a chemical co-precipitation method. The performances of the transformers with either air core or ferro-nanofluid at different concentrations of nanoparticles of 0.25, 0.5, 0.75, and 1 M were measured and simulated at frequencies ranging from 100 kHz to 100 MHz. The experimental results indicated that the inductance and coupling coefficient of coils grew with the increment of the ferro-nanofluid concentration. The presence of ferro-nanofluid increased resistance, yielding to the decrement of the quality factor, owing to the phase lag between the external magnetic field and the magnetization of the material.

DNA detection using commercial mobile phones.

This study investigates the feasibility of using mobile phones cameras for DNA detection. DNA amplification uses the convective polymerase chain reaction (cPCR) technique due to its simple mechanism, which requires no thermal cycling control. Fluorescence increment analysis and information entropy analysis are employed separately to determine whether the test samples contain target DNA (Positive) or not (Negative). The fluorescence increment method uses the brightness of the captured images before and after DNA amplification to calculate ΔF. ΔF values above a threshold level indicate that the test sample is positive. The information entropy method defines the probability, P(C/X), which indicates whether the fluorescence image tends towards a specific shape. If a DNA template is successfully amplified, the captured fluorescence image should be a perfect circle. P(C/X) provides a threshold of 0.5 to identify a circle and values above 0.5 indicate the test sample is positive. Experimental results show that P(C/X) is more effective than ΔF for determining DNA detection results. The information entropy analysis method is applied to ten mobile phones of three different brands equipped with camera sensors, which have pixel numbers ranging from 120 M to 800 M. The clinical evaluation study (n = 60) for screening hepatitis B virus (HBV) plasmid samples shows that the accuracy rate of all models of mobile phones ranges from 85% to 100%. This illustrates that successful DNA detection can be achieved using the most widely deployed electronic device.

Energy saving effects of wireless sensor networks: a case study of convenience stores in Taiwan.

Wireless sensor network (WSN) technology has been successfully applied to energy saving applications in many places, and plays a significant role in achieving power conservation. However, previous studies do not discuss WSN costs and cost-recovery. The application of WSNs is currently limited to research and laboratory experiments, and not mass industrial production, largely because business owners are unfamiliar with the possible favorable return and cost-recovery on WSN investments. Therefore, this paper focuses on the cost-recovery of WSNs and how to reduce air conditioning energy consumption in convenience stores. The WSN used in this study provides feedback to the gateway and adopts the predicted mean vote (PMV) and computational fluid dynamics (CFD) methods to allow customers to shop in a comfortable yet energy-saving environment. Four convenience stores in Taipei have used the proposed WSN since 2008. In 2008, the experiment was initially designed to optimize air-conditioning for energy saving, but additions to the set-up continued beyond 2008, adding the thermal comfort and crowds peak, off-peak features in 2009 to achieve human-friendly energy savings. Comparison with 2007 data, under the same comfort conditions, shows that the power savings increased by 40% (2008) and 53% (2009), respectively. The cost of the WSN equipment was 500 US dollars. Experimental results, including three years of analysis and calculations, show that the marginal energy conservation benefit of the four convenience stores achieved energy savings of up to 53%, recovering all costs in approximately 5 months. The convenience store group participating in this study was satisfied with the efficiency of energy conservation because of the short cost-recovery period.

Chip-oriented fluorimeter design and detection system development for DNA quantification in nano-liter volumes.

The chip-based polymerase chain reaction (PCR) system has been developed in recent years to achieve DNA quantification. Using a microstructure and miniature chip, the volume consumption for a PCR can be reduced to a nano-liter. With high speed cycling and a low reaction volume, the time consumption of one PCR cycle performed on a chip can be reduced. However, most of the presented prototypes employ commercial fluorimeters which are not optimized for fluorescence detection of such a small quantity sample. This limits the performance of DNA quantification, especially low experiment reproducibility. This study discusses the concept of a chip-oriented fluorimeter design. Using the analytical model, the current study analyzes the sensitivity and dynamic range of the fluorimeter to fit the requirements for detecting fluorescence in nano-liter volumes. Through the optimized processes, a real-time PCR on a chip system with only one nano-liter volume test sample is as sensitive as the commercial real-time PCR machine using the sample with twenty micro-liter volumes. The signal to noise (S/N) ratio of a chip system for DNA quantification with hepatitis B virus (HBV) plasmid samples is 3 dB higher. DNA quantification by the miniature chip shows higher reproducibility compared to the commercial machine with respect to samples of initial concentrations from 10(3) to 10(5) copies per reaction.

Real-time PCR machine system modeling and a systematic approach for the robust design of a real-time PCR-on-a-chip system.

Chip-based DNA quantification systems are widespread, and used in many point-of-care applications. However, instruments for such applications may not be maintained or calibrated regularly. Since machine reliability is a key issue for normal operation, this study presents a system model of the real-time Polymerase Chain Reaction (PCR) machine to analyze the instrument design through numerical experiments. Based on model analysis, a systematic approach was developed to lower the variation of DNA quantification and achieve a robust design for a real-time PCR-on-a-chip system. Accelerated lift testing was adopted to evaluate the reliability of the chip prototype. According to the life test plan, this proposed real-time PCR-on-a-chip system was simulated to work continuously for over three years with similar reproducibility in DNA quantification. This not only shows the robustness of the lab-on-a-chip system, but also verifies the effectiveness of our systematic method for achieving a robust design.

Applications of ferro-nanofluid on a micro-transformer.

An on-chip transformer with a ferrofluid magnetic core has been developed and tested. The transformer consists of solenoid-type coil and a magnetic core of ferrofluid, with the former fabricated by MEMS technology and the latter by a chemical co-precipitation method. The performance of the MEMS transformer with a ferrofluid magnetic core was measured and simulated with frequencies ranging from 100 kHz to 100 MHz. Experimental results reveal that the presence of the ferrofluid increases the inductance of coils and the coupling coefficient of transformer; however, it also increases the resistance owing to the lag between the external magnetic field and the magnetization of the material.