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Abstract This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two‐terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low‐current level (µA) in the forming process, a stable memory‐switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired‐pulse facilitation/depression, potentiation/depression, spike‐amplitude‐dependent plasticity, and spike‐number‐dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high‐current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free‐drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model.

This study analyzes the potential for energy saving through the integration of a photovoltaic-thermal (PVT) system with a heat pump (HP) in a community with a small-scale thermal network. An empirical study was conducted in a community to analyze and compare the performance of a HP and small-scale thermal network with hub thermal energy storage system that utilizes the surplus thermal energy produced by a PVT system installed in a residential house from other buildings with that of an existing electric hot water boiler. The energy saving analysis of the proposed system revealed that the energy consumption could be reduced by more than 70 % compared to the existing system. The HP exhibited a COP ranging between 4.2 and 4.7. Throughout the experiment, the integrated system collected an average of 23.77 kWh of heat energy daily from the PVT units. The annual domestic hot water load coverage from PVT through the thermal network was found to be 50.4 %. These results indicate that PVT systems combined with thermal networks and HPs can effectively reduce energy consumption in individual buildings and enhance energy efficiency within communities.

As the demand for bio‐inspired neuromorphic systems grows, memristor has emerged as a pivotal component in artificial synaptic devices. This study delves into the advantages and limitations of the one‐transistor‐one‐resistor (1T‐1R) and 0T‐1R architectures for memory array configurations. A significant enhancement in the memristor's on/off ratio, surpassing 103, achieved by integrating both an overshoot suppression layer (OSL) and an ultra‐thin AlN oxygen barrier layer (OBL) is also reported. The concurrent insertion of an AlOx OSL is essential for manifesting the self‐compliance attribute in 4F2 memristor arrays. Evaluations of a 24 × 24 array embedded with OSL and OBL reveal a substantial reduction in retention variation. In terms of functionality, a 7.13‐fold decline in vector‐matrix multiplication error is observed, accentuating the potential of this approach for neural network synapse applications. The analog reset characteristics of the OBL memristor facilitate over 25 multilevel states. Furthermore, the adoption of nonnegative weights presents an avenue to potentially double synaptic integration density. Through simulation program with integrated circuit emphasis simulations, the necessity of nonnegative and 16‐level quantized conductance in balancing power consumption without accuracy loss at the image classification is validated.

The decarbonization of buildings and communities has become a requirement for sustainability. In this regard, South Korea has set a target of net-zero-energy buildings, wherein all public buildings with a floor area exceeding 500 m2 must cover 80% of the primary energy consumption using renewable energy systems by 2030. This study investigated the improvement and maximization of energy from renewable energy systems through energy sharing between buildings by retrofitting an existing community. The feasibility of achieving a positive energy balance was investigated via simulations. The existing community is comprised of two residential houses and two office buildings. Through the retrofit design planning of the existing community, the total capacity of renewable energy could be applied at 75.4 kW after renovation. Through simulation, power generation rates of the building integrated photovoltaic (BIPV) and photovoltaic and thermal (PVT) systems were analyzed. The renewable energy generation rates were 2.48 times higher when using BIPV and PVT systems. Therefore, the analysis of the annual energy balance of the community indicated that 46.2% of the surplus energy can be generated via renewable energy systems, compared with that of conventional community. Thus, the retrofit community can be renovated into a positive energy community.

In this work, we compare the resistive switching characteristics between Ti/ZrOX/TiN and Ti/ZrOX/HfAlOX/TiN. The bilayer structure of the ZrOX-based device enables power consumption reduction owing to a lower forming voltage and compliance current. Moreover, the on/off ratio of the Ti/ZrOX/HfAlOX/TiN device (>102) is higher than that of the Ti/ZrOX/TiN device (>10). We use the 1/f noise measurement technique to clarify the transport mechanism of the Ti/ZrOX/HfAlOX/TiN device; consequently, ohmic conduction and Schottky emission are confirmed in the low- and high-resistance states, respectively. In addition, the multilevel cell, potentiation, and depression characteristics of the Ti/ZrOX/HfOX/TiN device are considered to assess its suitability as a neuromorphic device. Accordingly, a modified National Institute of Standards and Technology database simulation is conducted using Python to test the pattern recognition accuracy.

Direct-expansion solar-assisted heat pumps (DX-SAHP) are increasingly used in heating applications owing to their high efficiency. In this study, the energy performance of a DX-SAHP for a distributed thermal prosumer in a thermal network was investigated. A thermal network for supplying hot water, established in the eco-friendly energy town of Jincheon, was selected as the test site, and a daycare center was selected as the pilot building. A control logic for serving produced thermal energy to the thermal network was developed as an operation of thermal prosumer. Experiments were conducted during the cooling and heating seasons, and the results of three operation cases showed that the proposed prosumer operation can transfer the thermal energy within the thermal network. The daily load of domestic hot water was approximately 50–100 kWh, and the daily coefficient of performance of the DX-SAHP ranged from 2.0 to 5.0. The coefficient of performance of the DX-SAHP was found to be strongly dependent on the outdoor air temperature, solar irradiation, and outlet water temperature. During the operation, the DX-SAHP contributed to achieving over 66% savings in the operating energy compared with a conventional electric heater.

Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco‐friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with biorealistic synaptic plasticity for energy efficient learning processes is still challenging. Herein, a biodegradable and flexible polymer‐based memristor, suitable for the spike‐dependent learning process, is demonstrated. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA‐based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike‐dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco‐friendly wearable intelligent systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166603245.58711630/v1.

The utilization of renewable energy sources have gained significant attention in recent years for greenhouse that consumed lots of cooling and heating energy. This study investigated a solar thermal and seasonal thermal-based renewable energy convergence system. This system consists of a solar thermal system, tank- and borehole-type seasonal thermal energy storage, multi-source (i.e., air and water) heat pumps, and ground-source heat pumps. The detailed control logic of the proposed system was implemented, and the system was constructed in a test greenhouse at the Purme Yeoju Farm in South Korea. During the cooling season, the multi-source heat pumps were operated as air-source heat pumps, which showed a coefficient of performance (COP) only 0.5 less than that of the ground source heat pump. The efficiency of the solar thermal system was determined to be 36.6% throughout the investigation period, and the heat storage efficiency of the tank-type seasonal thermal energy storage was 75.7%. Consequently, the proposed system showed a COP of 2.9 during the heating season. The proposed system shows that over 70% of the operating energy can be saved, which is unachievable when using conventional electric heaters.

In this work, a synaptic weight transfer method for a neuromorphic system based on resistive-switching random-access memory (RRAM) is proposed and validated. To implement the on-chip trainable neuromorphic system which utilizes large-scale hardware synapse units, a fast and reliable write scheme needs to be established. Based on the experimental results, it is confirmed that the gradual set and full reset operation is the most suitable operation scheme for fast programming due to the fundamental reliability characteristics of the resistive-switching memory cell. Also, the superiority of this programming method using the proposed RRAM compact model is demonstrated. In addition, a one weight/one synaptic device structure is newly adopted for realizing high-density synapse arrays by using a nonnegative weight constraint in supervised learning. Finally, the pattern recognition accuracies obtained at the software and hardware levels are compared.

Abstract In this study, we propose an effective strategy for achieving the flexible one organic transistor–one organic memristor (1T–1R) synapse using the multifunctional organic memristor. The dynamics of the conductive nanofilament (CF) in a hydrophobic fluoropolymer medium is explored and a hydrophobic fluoropolymer-based organic memristor is developed. The flexible 1T–1R synapse can be fabricated using the solution process because the hydrophobic fluorinated polymer layer is produced on the organic transistor without degradation of the underlying semiconductor. The developed flexible synapse exhibits multilevel conductance with high reliability and stability because of the fluoropolymer film, which acts as a medium for CF growth and an encapsulating layer for the organic transistor. Moreover, the synapse cell shows potential for high-density memory systems and practical neural networks. This effective concept for developing practical flexible neural networks would be a basic platform to realize the smart wearable electronics.

Neuromorphic electronics attract significant attention as a new computing architecture. Despite much effort for achieving practical neuromorphic systems, it is still challenging to construct a synapse array ideal for complex neural networks. Herein, a novel strategy for developing a highly integrated crossbar array of a one‐selector–one‐memory (1S–1R) synapse by systematically engineering ion injection is demonstrated. In the proposed synapse, an electrochemical metallization (ECM) memristor consisting of unstable filaments and a typical ECM device with stable filaments act as a selector with a low leakage current and a stable memory device, respectively. To overcome the voltage‐matching issues in constructing the 1S–1R synapse with high integration density, ion injection related with the electrical properties is optimized in the ECM devices via the distribution of active metal nanoparticles at the interface. The developed synapse possesses a high on/off ratio, superior selectivity, low operating current, and stable multilevel conductance, compared to the previously reported devices. High feasibility for complex neuromorphic systems is demonstrated, and the neural network based on the developed synapse array exhibits reliable parallel computation with high energy efficiency. This promising concept of realizing complex neuromorphic electronics is a fundamental building block for the practical artificial intelligence.

In this study, the electrical characteristics of TiN/SiNx/TiN and TiN/Ag/SiNx/TiN RRAMs were thoroughly investigated through I-V measurements. Our novel Ag-inserted silicon nitride-based resistive switching memory (RRAM) achieved switching operation at lower voltages and lower current levels compared to the conventional silicon nitride RRAM that does not have an Ag insertion layer. These enhanced characteristics enable low power operation, one of the main goals of RRAM research. Gradual conductance modulation is also possible in this device with constant voltage pulses being 50% lower than that seen in conventional devices, this property fulfills an important requirement that makes our device suitable for use as a neuromorphic synapse device. The conduction mechanism of our Ag-inserted device was further analyzed through I-V measurements and compared with a control group. Based on these data, a 2D Monte Carlo simulation was implemented to investigate the mechanism behind the new behavior displayed by our Ag-inserted silicon nitride RRAM. A resistive network model was used to calculate the voltage and electric field distribution, and then the motion of the particles was simulated by taking into account its relation to the heat generated inside the device.

This study investigates the economic benefits of solar thermal and seasonal thermal energy storage based on a renewable energy conversion system for greenhouses. The proposed system consists of solar collectors, seasonal thermal energy storage, hybrid-source heat pumps, and ground-source heat pumps. The heat generated from the proposed system was stored in two types of seasonal thermal energy storage and supplied to the greenhouse using Purme Yeoju Farm in South Korea for experimental analysis. Based on the experimental data gathered over a heating system, the economic benefits of operating cost savings and carbon trading with the greenhouse gas emission reduction of the proposed system were investigated by comparing to a conventional heating season using oil and electric boilers. From October 2021 to March 2022, approximately 38.4% of the total 482 MWh of heat was supplied either directly or indirectly through the solar system. In addition, the coefficient of the performance of the entire proposed system was calculated to be 2.28. Both the operating cost savings and greenhouse gas emission reductions of the proposed system showed over 73% and 82% compared with those of conventional systems.

Abstract This letter presents dual functions including selector and memory switching in a V/SiO x /AlO y /p++Si resistive memory device by simply controlling compliance current limit (CCL). Unidirectional threshold switching is observed after a positive forming with low CCL of 1 μA. The shifts to the V-electrode side of the oxygen form the VO x layer, where the threshold switching can be explained by the metal-insulation-transition phenomenon. For higher CCL (30 μA) applied to the device, a bipolar memory switching is obtained, which is attributed to formation and rupture of the conducting filament in SiO y layer. 1.5-nm-thick AlO y layer with high thermal conductivity plays an important role in lowering the off-current for memory and threshold switching. Through the temperature dependence, high-energy barrier (0.463 eV) in the LRS is confirmed, which can cause nonlinearity in a low-resistance state. The smaller the CCL, the higher the nonlinearity, which provides a larger array size in the cross-point array. The coexistence of memory and threshold switching in accordance with the CCL provides the flexibility to control the device for its intended use.

To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption. To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental data. The conditions that had a significant effect on energy consumption were analyzed to be: (i) conditions related to the building design—wall, floor, and window area ratio, the window-to-wall ratio (WWR), the window-to-floor area ratio (WFR), and the azimuth, and (ii) the internal conditions of the building—the illuminance, occupancy density, plug load, and frequency of room utilization. The room conditions derived through analysis were considered in each sample, and the final influential building energy consumption factors were derived by using them in a decision tree as being the WFR, window area ratio, floor area ratio, wall area ratio, and frequency of use. Furthermore, four room types were classified by combining the room conditions obtained from the key factor classifications derived in this study.

This study evaluated the energy saving potential of renewable energy generation systems based on integrated solar energy in an urban environment. The solar city concept was implemented using photovoltaic (PV) and solar thermal systems. As a case study, the Sejong national pilot smart city in South Korea was selected to evaluate the renewable energy penetration rate. For evaluating the proposed renewable energy systems, the electrical and thermal loads of the smart city were estimated using field measurement data. Then, the renewable energy penetration rate of the city was evaluated. The HomerPro software was used to analyze the PV generation and operating energy consumption of the natural gas (NG) generator with a district heating network. The thermal load-supporting potential of the solar thermal system was estimated using the TRNSYS software. The results showed that the proposed urban integrated renewable energy system could meet over 30% of the renewable energy penetration rate and the levelized cost of energy and total net present cost was 7% lower than the base case system (i.e., NG generator). The proposed system also exhibited 38% less CO2 emissions than the base case system.

Due to the wide-spread use of photovoltaic (PV) systems, interest regarding economic benefit and energy-sharing from surplus electricity has been raised. In this study, decentralized thermal and electric convergence energy prosumers for an energy-sharing community are proposed. For the convergence energy system, a simultaneous heating and cooling heat pump (SHCHP) system integrated with a thermal network is proposed, and the energy performance and operating energy savings of the proposed system were investigated. A smart village located in the Busan Eco Delta Smart City was selected as a case study for the simulation analysis. Experimental data of the heat pump system were used to analyze the SHCHP. The analysis results showed that the proposed system could provide over 53% and 86% of the load cover and supply cover factors, respectively. The proposed system can earn economic benefits, such as energy trading from the surplus electricity of PV systems and thermal energy produced by an SHCHP, more than 30.5 times those of conventional air-source heat pump systems. These benefits mainly originate that a conventional system can trade the surplus electricity from a PV system but the proposed system can trade produced thermal energy from SHCHPs and the surplus electricity from PV systems.

The demand for district heating and cooling systems in block units with a heat pump that utilizes various unused energy sources for energy supply has been increasing. This study investigated experimentally the ground source heat pump (GSHP) and sewage water source heat pump (SWSHP) facilities used in block cooling and heating networks. Then, a heat pump performance prediction model was derived for utilization in future designs. Operational data for heating and cooling energy supply from an experimental site were investigated for the period between 2018 and 2020. During the cooling season, the coefficient of performance (COP) of the GSHP was approximately 4.1, and that of the SWSHP was approximately 2.9. The cooling performance of the SWSHP gradually decreased because of the fouling. The COP of the GSHP and SWSHP during the heating season was approximately 3.6 and 3.4, respectively. The results also demonstrated that, if fouling in the SWSHP can be prevented or reduced, the acquired COP can be similar to that of the GSHP. The derived prediction model serves as a good reference for engineers who require information on the performance of field operations.

District cooling and heating methods that can utilize highly efficient heat pumps and various unused new and renewable types of energy are required to achieve low carbon emissions and zero energy usage in buildings and community units. The technical requirements for the implementation of decentralized thermal networks and heat trading are increasing, both for thermal networks in new buildings and for those remodeled based on existing centralized thermal networks. In this study, a conventional centralized thermal network was implemented as a decentralized thermal network and the possibility of heat prosumers feeding thermal networks was demonstrated experimentally. A real-scale plant was constructed by employing unused thermal energy facilities as prosumers in a school and childcare center based on the existing small-scale block heating and cooling thermal network. The decentralized thermal network and heat prosumer concepts were proven through operation experiments performed on the constructed system in summer and winter. An economic benefit can be achieved by increasing the peak power cost. The experimental results also showed that the proposed bi-directional heat trading reduced carbon emissions by 12.7% compared with conventional centralized thermal systems.

Due to increased grid problems caused by renewable energy systems being used to realize zero energy buildings and communities, the importance of energy sharing and self-sufficiency of renewable energy also increased. In this study, the energy performance of an energy-sharing community was investigated to improve its energy efficiency and renewable energy self-sufficiency. For a case study, a smart village was selected via detailed simulation. In this study, the thermal energy for cooling, heating, and domestic hot water was produced by ground source heat pumps, which were integrated with thermal energy storage (TES) with solar energy systems. We observed that the ST system integrated with TES showed higher self-sufficiency with grid interaction than the PV and PVT systems. This was due to the heat pump system being connected to thermal energy storage, which was operated as an energy storage system. Consequently, we also found that the ST system had a lower operating energy, CO2 emissions, and operating costs compared with the PV and PVT systems.

Brain-inspired artificial synaptic devices and neurons have the potential for application in future neuromorphic computing as they consume low energy. In this study, the memristive switching characteristics of a nitride-based device with two amorphous layers (SiN/BN) is investigated. We demonstrate the coexistence of filamentary (abrupt) and interface (homogeneous) switching of Ni/SiN/BN/n++-Si devices. A better gradual conductance modulation is achieved for interface-type switching as compared with filamentary switching for an artificial synaptic device using appropriate voltage pulse stimulations. The improved classification accuracy for the interface switching (85.6%) is confirmed and compared to the accuracy of the filamentary switching mode (75.1%) by a three-layer neural network (784 × 128 × 10). Furthermore, the spike-timing-dependent plasticity characteristics of the synaptic device are also demonstrated. The results indicate the possibility of achieving an artificial synapse with a bilayer SiN/BN structure.

The main objective of this study was to develop a thermoelectric heat pump and liquid desiccant system based on a dedicated outdoor air system (THPLD-DOAS). An internally-cooled and -heated liquid desiccant system was used and a thermoelectric heat pump (THP) served as the desiccant cooling and heating energy source for dehumidification and regeneration of the desiccant solution, respectively. In order to investigate the energy-saving potential of the proposed system, its thermal performance and operating energy consumption during the cooling season were compared to those of a conventional dedicated outdoor air system with a ceiling radiant cooling panel system (DOAS-CRCP). Detailed simulations for each system were conducted under hot and humid climatic conditions. Their thermal performance under various room sensible heat factor (RSHF) conditions was evaluated to observe the energy performance, depending on the dehumidification performance, of the liquid desiccant system integrated with the THP. The results showed that the coefficient of performance (COP) of the THP ranged from 0.8 to 1.2 to maintain a sufficient dehumidification rate. The operating energy of the THPLD of the proposed system was 6.6% to 16.0% less than that of the chiller operating energy of a conventional DOAS. Consequently, the proposed system consumed 0.6–23.5% less operating energy compared to the conventional DOAS.

In this work, we compare the resistive switching characteristics between Ti/ZrOX/TiN and Ti/ZrOX/HfAlOX/TiN. The bilayer structure of the ZrOX-based device enables power consumption reduction owing to a lower forming voltage and compliance current. Moreover, the on/off ratio of the Ti/ZrOX/HfAlOX/TiN device (>10²) is higher than that of the Ti/ZrOX/TiN device (>10). We use the 1/f noise measurement technique to clarify the transport mechanism of the Ti/ZrOX/HfAlOX/TiN device; consequently, ohmic conduction and Schottky emission are confirmed in the low- and high-resistance states, respectively. In addition, the multilevel cell, potentiation, and depression characteristics of the Ti/ZrOX/HfOX/TiN device are considered to assess its suitability as a neuromorphic device. Accordingly, a modified National Institute of Standards and Technology database simulation is conducted using Python to test the pattern recognition accuracy. [ABSTRACT FROM AUTHOR]