Energy Usage Costs Research Articles

The impact of Nanomaterials on energy-centric form-finding of educational buildings in semi-Arid climate

In the modern world, the use of novel technologies in architecture has become highly significant and transformative for human-environment interactions. One of the most critical concerns in architecture is achieving optimal forms and selecting suitable materials for effective design across diverse climatic zones. Also, adopting innovative climate design methods in public spaces, such as educational buildings, is essential due to their strategic urban locations and diverse user populations. Therefore, the research conducted in this study focuses on two main aspects: optimizing building form based on energy consumption and solar radiation received by vertical surfaces, and, selecting appropriate nanomaterials for building surface to reduce energy usage and maintenance costs. This study begins with theoretical foundations, defining the key terms through a comprehensive review of relevant literature. Then, four identical classroom modules with consistent height and floor levels are proposed, and Energy Plus software is used to evaluate the energy consumption based on a module simulation in initial forms of square and rectangle with varying proportions. The best module and orientation is determined using specified climatic data of the coldest and the hottest days of the year. Further, investigations involve combining these modules in various layouts, emphasizing those that align with the functional requirements of educational spaces. Finally, two parameters of energy consumption and solar radiation on vertical surfaces are measured during specified time interval between sunrise and sunset. The results indicate that among the four proposed modules, the 18x18 module with a north-south orientation is the most optimal in the semi-arid climate of Tehran, and therefore, Type 3 layout demonstrates the best performance for energy consumption. This is while by incorporating selected nanotechnology (self-cleaning nanomaterial paint), energy usage decreases in all layouts, regardless of the season.

Transactive energy management for CIES considering tri-level hybrid game-theoretic approaches with multiple weight allocation factors

In city integrated energy systems, to reduce the costs of energy production and usage, it is extremely important for different entities to design proper energy management approaches since they have diverse behaviours and benefits. The hierarchy of the system is naturally divided into three layers, and hybrid game theories are employed on the basis of the vertical master–slave and horizontal equal cooperation characteristics among energy providers, aggregators, and users. First, to reflect the interactions between different levels' entities, Stackelberg game theory is adopted vertically, and the model complexity is lessened by the KKT conditions. Then, for energy providers, the asymmetric bargaining game is improved by combining multiple weight allocation factors, which can help reduce renewable energy fluctuations and encourage the consumption of clean energy to reduce the carbon emissions of the system. Also, the results show that the proposed method can enhance the utility of renewable energy generation units by approximately 64 % to 69 %. Finally, the optimality and effectiveness of the proposed game theoretic approaches are verified in case studies and in strict mathematical proofs. In addition, the impact mechanisms on the demand response from the perspectives of the user load and price sensitivity are analysed. To explore pricing methods that are more suitable for user aggregators, two pricing strategies are designed for the user aggregator and compared in terms of computation time and revenue. The results show that a unified pricing strategy can increase the aggregator's revenue by 1.24 % and reduce the computation time by 24.47 %.

Kinetics and mechanisms of non-radically and radically induced degradation of bisphenol A in a peroxymonosulfate-chloride system

Bisphenol A, a hazardous endocrine disruptor, poses significant environmental and human health threats, demanding efficient removal approaches. Traditional biological methods struggle to treat BPA wastewater with high chloride (Cl−) levels due to the toxicity of high Cl− to microorganisms. While persulfate-based advanced oxidation processes (PS-AOPs) have shown promise in removing BPA from high Cl− wastewater, their widespread application is always limited by the high energy and chemical usage costs. Here we show that peroxymonosulfate (PMS) degrades BPA in situ under high Cl− concentrations. BPA was completely removed in 30 min with 0.3 mM PMS and 60 mM Cl−. Non-radical reactive species, notably free chlorine species, including dissolved Cl2(l), HClO, and ClO− dominate the removal of BPA at temperatures ranging from 15 to 60 °C. Besides, free radicals, including •OH and Cl2•−, contribute minimally to BPA removal at 60 °C. Based on the elementary kinetic models, the production rate constant of Cl2(l) (32.5 M−1 s−1) is much higher than HClO (6.5 × 10−4 M−1 s−1), and its degradation rate with BPA (2 × 107 M−1 s−1) is also much faster than HClO (18 M−1 s−1). Furthermore, the degradation of BPA by Cl2(l) and HClO were enlarged by 10- and 18-fold at 60 °C compared to room temperature, suggesting waste heat utilization can enhance treatment performance. Overall, this research provides valuable insights into the effectiveness of direct PMS introduction for removing organic micropollutants from high Cl− wastewater. It further underscores the critical kinetics and mechanisms within the PMS/Cl⁻ system, presenting a cost-effective and environmentally sustainable alternative for wastewater treatment.

Optimizing renewable energy integration in new districts: Power-to-X strategies for improved efficiency and sustainability

To achieve optimal renewable energy self-sufficiency in new districts, it's crucial to efficiently manage surplus electricity or heat. One effective approach is ‘sector coupling,’ which increases self-consumption by redirecting excess energy via heat pumps (Power-to-Heat) or producing hydrogen (Power-to-Gas). Integrating electric vehicles and storage solutions (Power-to-Power) further enhances system flexibility. Moreover, the implementation of energy communities offers not just an uptick in self-consumption but also encourages consumers to embrace renewable energy technologies. This study evaluates the integration of renewable energy within urban districts through ‘sector coupling’ strategies, aiming to enhance self-sufficiency and consumption by managing surplus electricity and heat. Focusing on a renovated district in Southern Italy, it compares Power-to-X strategies—such as Power-to-Heat, Power-to-Gas, and Power-to-Power—in terms of their impact on primary energy usage, CO2 emissions, and financial costs. Dynamic simulations show these strategies can reduce CO2 emissions by 30 % and energy use by 20 % on average, offering valuable insights for urban energy planning and policy.

Carbon Footprint Reduction for Sustainable Data Centers in Real-Time

As machine learning workloads are significantly increasing energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry-standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.

Research on energy consumption in household sector: a comprehensive review based on bibliometric analysis

Households are an important sector in carrying out human development activities, accounting for more than 30% of the total global energy consumption. The continued growth of household energy consumption (HEC) and carbon emissions is threatening economic and environmental sustainability. This review focuses on the research in the field of HEC and conducts a bibliometric analysis of research articles from the Web of Science Core Collection since 2000. The results show that: 1) HEC research has undergone rapid development since 2014, and interdisciplinary fusion and collaborative research have become dominant trends. 2) Keyword co-occurrence analysis clearly identifies the current urgent themes, including energy demand and its determinants, environmental impact factors and assessments, and energy-saving technologies and emission reduction measures. 3) The analysis of citations reveals that economic models, such as input-output models and life cycle assessment, are frequently employed in the field of HEC. Based on a summary of household energy-saving and emissions reduction work, this paper critically discusses the limitations of existing measures such as smart home technology, sustainable energy systems, and behavioral interventions. The main directions for promoting household energy-saving development in the future are identified: including improving the security and customer engagement of smart home technology, focusing on the availability and stability of sustainable energy, and paying more attention to low-income and aging households in behavioral intervention measures. One of the important obstacles facing research is how to reduce energy management efficiency and usage costs through technology and policy.

Modeling techno-economic multi-objectives of smart homes considering energy optimization and demand management

The research suggests an approach that prioritizes customer needs and aims to reduce energy expenses while safeguarding customer privacy. Furthermore, it is recommended that smart homes incorporate a home energy management system to optimize appliance energy consumption. Conversely, the introduction of demand-side management addresses the energy management challenges faced by smart households. The main goal of this approach is to decrease energy usage and electricity costs for customers. Moreover, it enhances user satisfaction while waiting at common intervals. The primary emphasis of this study is on a smart residence furnished with energy management technology and smart home gadgets capable of supplying electricity to the grid. These objectives are considered distinct aspects in the multi-objective optimization issue stemming from this approach. The study utilizes the grasshopper optimization algorithm (GOA) to optimize battery and home appliance scheduling in smart homes with flexible devices. The goal is to reduce the overall cost of microgrid systems through demand-side management implementation. This comparison highlights the superiority of the proposed method in optimizing energy consumption and reducing carbon emissions in a variety of scenarios. By achieving lower energy costs and carbon emissions while maintaining a comfortable indoor environment, the proposed method proves to be a highly effective and sustainable solution for energy management in buildings. These simulation results provide strong evidence of the method’s potential to significantly impact energy efficiency and environmental sustainability in real-world applications. Furthermore, the consistent minimization of the discomfort index showcases the method’s ability to prioritize occupant comfort while still achieving significant energy savings and emissions reductions. Overall, the comparison with other algorithms solidifies the effectiveness and practicality of the proposed method in addressing the complex challenges of energy management and sustainability in smart homes.

Optimization of renewable energy usage in public transportation: Mathematical model for energy management of plug-in PV-based electric metrobuses

In which internal combustion engines are used, public transportation systems undoubtedly have an important role in increasing carbon emissions. On the other hand, to reduce the carbon footprint and satisfy sustainability targets, the demand for electric vehicles with high energy conversion efficiency is increasing. At that point, various studies have been carried out to find out greener solutions in recent years, and it is demonstrated that it is also important to electrify public vehicles. This study contributes to the related literature and focuses on metrobuses, which are frequently used in the public transportation sector, and have garages in Istanbul, Turkey's most populated city. An environmentally friendly Metrobus Charging Station Optimization Model, which is integrated with the grid and renewable energy systems and can exchange energy with the grid, when necessary, is designed for the IETT Avcılar Metrobus garage where the metrobuses are located which has static conditions and optimal scheduling. The single objective MILP maximizes the usage of renewable energy by minimizing the cost of non-renewable energy usage and points out the best assignment of metrobuses to the scheduled departures. Finally, the model is solved in GAMS solver and it is observed that an environmentally friendly approach is not costly.

Implementation of renewable energy using smart light solar cell system for mosque energy efficiency

The outreach activity focuses on improving energy efficiency at Sirothol Mustaqim Mosque in Pasuruan Regency. The primary issue addressed is how to meet the lighting needs of the mosque while minimizing the use of PLN electricity and optimizing the use of lamps and electricity based on the number of congregants. The main objective is to implement renewable energy through solar panels to replace conventional electricity, analyze the efficiency of electricity usage based on lighting requirements, and enhance electricity efficiency based on time and lamp location. Expected outcomes include increased electricity usage efficiency, reduced operational costs for mosque management, and advanced scientific and technological knowledge in the general community. The participatory action research (PAR) method emphasizes active participation and collaboration between academics and the community. The project commences with a field survey to identify energy needs and the potential for solar energy sources on-site. Subsequently, project planning and design are carried out, followed by project implementation with training for mosque administrators. The results of the project's implementation include energy usage efficiency, the integration of renewable energy in a religious context, and contributions to understanding environmental responsibilities. The success of the implementation measure calculations for the usage time of 40-watt lamps using solar energy are provided, along with a comparison of PLN electricity costs and solar energy usage costs. The analyses demonstrate favorable energy efficiency using solar power.

THE EFFECT OF ECONOMIZER APPLICATION ON ENERGY ECONOMY IN STENTER MACHINES USED

This study investigates the impact of economizer application on reducing energy consumption in the textile industry. It starts by analyzing the influence of traditional drying methods on both energy usage and operational costs. Next, it explains the operation of ramie machines and economizer technology. The primary focus is on the contributions of economizer application to energy efficiency. This includes highlighting the reduction in energy consumption and cost savings in operations. The study also underscores the importance of economizer application for environmental sustainability by helping to reduce greenhouse gas emissions. Furthermore, it discusses the investment cost and payback period of economizers, emphasizing how they can enhance a company's economic competitiveness. Real-world examples and success stories are provided to demonstrate the practicality of implementing economizers. In conclusion, this research underscores the significance and potential of economizer applications in improving energy economics within the textile sector. This technology not only enables energy savings but also contributes to environmental sustainability, supporting the creation of a more sustainable future in the textile industry. It also compares the energy consumption of a single-pass ramie machine with and without economizer use, assuming a 24-hour daily operation for 300 days, while thoroughly evaluating the associated advantages and disadvantages.

Comparative Analysis of Residential Energy Consumption in Selected Areas of Cantilan Surigao del Sur

Energy consumption is considered as one of the most crucial issues in our daily lives, yet, the fundamental understanding of how energy assessment and policies are designed remained as one of the biggest barriers because of the complexity. The study is focused on analyzing the residential energy consumption in selected barangays in the municipality of Cantilan, Surigao del Sur, Mindanao Philippines. The study provides well-documented data as basis for establishing policy for potential energy usage reductions and cost savings of the municipality. The study investigates the profile of the respondents, profile of household based on construction type; and residents’ contributory factors and practices on energy consumption. The descriptive research design was employed through survey and informal interviews from the two most populated barangays in the municipality of Cantilan. A validated researcher-made questionnaire served as the main tool in the data gathering. Based on the findings, the contributory factors on energy consumption and the conservation practices of the respondent barangays mirrored their socio-economic status. Most respondents fall into low-income households, where only those essential appliances useful in their day-to-day activities are the priorities and things they afforded. Although most households only have a few appliances, the average monthly electric expense has increased due to high energy costs and frequent energy interruptions. The respondents’ conservational habits are reflected in their energy consumption. It can be deduced that understanding the consumers’ motivations of energy usage in a specific situation, will eventually lead to gaining insights of good energy consumption process in the area.

GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction

Cloud computing has revolutionized data storage, processing, and access in modern data center operations. Conventional data centers use enormous amounts of energy for server operation, power supply, and cooling. The processors produce heat while processing the data and therefore increase the center’s carbon footprint, and the rising energy usage and carbon emissions caused by data centers pose serious environmental challenges. Under these circumstances, energy-efficient green data centers are being used as a phenomenal source of sustainable modernization. This study proposes the implementation of the Green Energy Efficiency and Carbon Optimization (GEECO) model for enhancing energy usage. Within the data center, the GEECO model dynamically adjusts workload distribution and task assignment to balance performance and manage service-level reconciliation. The ability to identify possibilities for energy optimization and carbon emission reduction is possible through real-time monitoring of energy usage and workload demand. The results revealed a considerable increase in energy efficiency, with significant decreases in energy usage and related costs. The GEECO model provides a significant improvement in energy consumption and carbon emission reduction for the different introduced scenarios. This model’s introduction to practical application would be made possible by these improvements in the quantitative results. The approach of this study also has a positive impact on the environment by reducing carbon emissions. The resilience and practicality of the solution are also analyzed, highlighting the probability of widespread adoption and its associated improvements in the advancement of sustainable cloud computing.

New coordination framework for smart home peer-to-peer trading to reduce impact on distribution transformer

The emergence of distributed energy resources (DERs) is expected to result in an increase in the amount of power transmitted through the distribution power grid. If these DERs are not managed properly, the increased power transmission can cause faster degradation of distribution grid assets (pole-top transformers). To prevent this, measures can be taken to manage the DERs connected to a specific pole-top transformer. This paper presents a new coordination framework for home energy management system (HEMS)-integrated peer-to-peer (P2P) trading, with a focus on the impact of such trading on a Distribution Transformer. The proposed framework provides a comprehensive solution to manage the distribution of power within a smart grid environment by enabling HEMS to engage in P2P trading. This paper also examines optimal energy management in a smart neighborhood to minimize the total cost of energy usage. In addition, to prevent power peaks — that could create excessive power transmission and damage the distribution transformer, a cap within the flexibility bound of the household is placed on the total power households can draw/penetrate from/to the power grid. By optimizing P2P energy trading while considering the limitations of the distribution transformer, this approach enables a more efficient, reliable, and sustainable energy system.

Winner or loser? The bidirectional impact between geopolitical risk and energy transition from the renewable energy perspective

Increasingly complex global geopolitical situations are causing severe impacts on energy markets and making renewable energy (RE) the best energy option. Against fierce geopolitical conflicts, this investigation attempts to probe the bidirectional impact between geopolitical risk (GPR) and RE. By applying the rolling-window Granger causality test, we find that GPR positively and negatively impacts RE. The positive influence confirms that geopolitical competition increases the traditional energy usage cost and incentivises investment in the RE sector. This suggests GPR can be perceived as a winner in driving the energy transition. However, the rise in GPR reduces RE projects' attractiveness and inhibits the energy transition process's smooth progress. The adverse impact of RE on GPR indicates that RE is an effective channel for increasing energy diversity and reducing geopolitical events. However, the expansion of RE is vulnerable to national policy changes and critical resource competition, which induces new geopolitical conflicts. This finding is supported by the classic production model, emphasising that GPR and RE have mutual connections. To this end, governments should provide incentives for deploying RE projects to ensure energy security and promote energy transition.

Internet of things enabled parking management system using long range wide area network for smart city

As the Internet of Things (IoT) evolves, it paves the way for vital smart city applications, with the Smart Parking Management System (SPMS) standing as a prime example. This research introduces a novel IoT-driven SPMS that leverages Long Range Wide Area Network (LoRaWAN) technology, termed as IoT-SPMS-LoRaWAN, to surmount typical restrictions related to communication range, energy usage, and implementation cost seen in traditional systems. IoT-SPMS-LoRaWAN features intelligent sensing nodes that incorporate an Arduino UNO microcontroller and two sensors—a triaxial magnetic sensor and a waterproof ultrasonic sensor. These components collaboratively detect vehicle occupancy and transmit this data to the server via a LoRaWAN gateway. Notably, the integration of LoRa technology enables extensive network coverage and energy efficiency. Users are provided with real-time updates on parking availability via the accessible AllThingsTalk Maker graphical user interface. Additionally, the system operates independently, sustained by a solar-powered rechargeable battery. Practical testing of IoT-SPMS-LoRaWAN under various scenarios validates its merits in terms of functionality, ease of use, reliable data transmission, and precision. Its urban implementation is expected to alleviate traffic congestion, optimize parking utilization, and elevate awareness about available parking spaces among users. Primarily, this study enriches the realm of smart city solutions by enhancing the efficiency of parking management and user experience via IoT.

Solar energy as a sustainable source of energy for industrial applications in Sri Lanka: a conceptual mini-review

The world’s primary energy demand has increased significantly due to the industrial revolution and population growth, which create the demand for primary energy. The COVID-19 pandemic situation significantly damages the world’s energy network, which critically affects developing countries like Sri Lanka. Therefore, this paper has been developed to discuss an important framework for the use of solar energy as a suitable energy source for various industrial operations. The application of solar thermal energy and solar PV systems into industrial processes can minimize the economic cost of energy usage in the country. The points discussed in this conceptual mini-review can help policymakers identify and explore industrial windows that can be structured with solar energy for a sustainable energy future in Sri Lanka.

Energy‐efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads

AbstractCloud computing has become a critical infrastructure for modern society, like electric power grids and roads. As the backbone of the modern economy, it offers subscription‐based computing services anytime, anywhere, on a pay‐as‐you‐go basis. Its use is growing exponentially with the continued development of new classes of applications driven by a huge number of emerging networked devices. However, the success of Cloud computing has created a new global energy challenge, as it comes at the cost of vast energy usage. Currently, data centres hosting Cloud services world‐wide consume more energy than most countries. Globally, by 2025, they are projected to consume 20% of global electricity and emit up to 5.5% of the world's carbon emissions. In addition, a significant part of the energy consumed is transformed into heat which leads to operational problems, including a reduction in system reliability and the life expectancy of devices, and escalation in cooling requirements. Therefore, for the future generations of Cloud computing to address the environmental and operational consequences of such significant energy usage, they must become energy‐efficient and environmentally sustainable while continuing to deliver high‐quality services. In this article, we propose a vision for learning‐centric approach for the integrated management of new generation Cloud computing environments to reduce their energy consumption and carbon footprint while delivering service quality guarantees. In this article, we identify the dimensions and key issues of integrated resource management and our envisioned approaches to address them. We present a conceptual architecture for energy‐efficient new generation Clouds and early results on the integrated management of resources and workloads that evidence its potential benefits towards energy efficiency and sustainability.

A novel transactive integration system for solar renewable energy into smart homes and landscape design: A digital twin simulation case study

The use of solar renewable energy in landscape design is becoming increasingly popular as the world strives to reduce its reliance on fossil fuels. With the cost of solar energy falling, more and more people are turning to solar as a way to power their homes and businesses. In this article, we will explore the integration of solar renewable energy into landscape design through a case study. Using local photovoltaics along with suitable power storage systems, a household power management system is proposed to optimize the scheduling of demand-responsive appliances. The beta likelihood distribution function for sun irradiance is used to model the unpredictable behavior of photovoltaic energy production in residential power management systems. The PV system was integrated into the building’s existing landscape design by incorporating the system’s components into the existing landscape elements. This included the installation of a large solar array on the roof of the building, as well as the integration of solar panels into the existing garden beds and pathways. The solar array was also connected to the building’s existing electrical system, allowing the building to draw power from the solar array when needed. Among the major contributions of the study would be to optimize the planning of demand-responsive devices within a domestic power management system by using the improved leader particle swarm optimization method (ILPSO). The purpose would be to reduce peak-to-average ratios (PARs) and energy usage costs in the smart home. A number of scenarios are designed and simulated in a digital twin structure for a household user with various initial loads, uninterruptible deferrable, and interruptible deferrable devices using a real-time power cost program to demonstrate the performance of the suggested optimization method. Based on comparisons with various metaheuristics previously documented, the new ILPSO algorithm efficiently optimizes energy usage costs and PARs.

Realistic Home Energy Management System Considering the Life Cycle of Photovoltaic and Energy Storage Systems

Home Energy Management Systems (HEMSs) have become necessary due to energy security and climate change concerns. Scheduling the operating time of household appliances is one of the most effective strategies used by HEMSs to reduce electricity costs, with several studies proposing optimization strategies for scheduling home appliances to reduce the grid energy usage cost. This work considers energy usage costs from Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) in the appliance-scheduling strategy and energy flow management. The objectives are reducing the real electricity cost while maintaining a longer battery lifespan, reducing battery charging/discharging losses, and using PV power efficiently. To achieve this, we developed a pricing model of battery energy usage, in addition to modeling the PV energy usage cost based on the Levelized Cost of Energy (LCOE) for PV systems. PV-battery energy usage cost models were introduced into the optimization problem solved using the Augmented Grey Wolf Optimization (AGWO) and Particle Swarm Optimization (PSO) algorithms in MATLAB. We developed an efficient energy flow management algorithm. We collected real data from a home in Vigo, Spain, and simulated four scenarios. The results show that the proposed system using AGWO and PSO reduced the real cost by 25.87% and 25.98%, respectively. Compared with an existing energy-usage-pricing model, the AGWO reduced the energy losses by 40.429% and extended the battery lifespan by 68.282%. Similarly, the PSO reduced the energy losses by 45.540% and extended the battery lifespan by 84.56%. Moreover, the proposed system reached the breakeven point of the system in a shorter time.

Machine learning using magnetic stochastic synapses

The impressive performance of artificial neural networks has come at the cost of high energy usage and CO2 emissions. Unconventional computing architectures, with magnetic systems as a candidate, have potential as alternative energy-efficient hardware, but, still face challenges, such as stochastic behaviour, in implementation. Here, we present a methodology for exploiting the traditionally detrimental stochastic effects in magnetic domain-wall motion in nanowires. We demonstrate functional binary stochastic synapses alongside a gradient learning rule that allows their training with applicability to a range of stochastic systems. The rule, utilising the mean and variance of the neuronal output distribution, finds a trade-off between synaptic stochasticity and energy efficiency depending on the number of measurements of each synapse. For single measurements, the rule results in binary synapses with minimal stochasticity, sacrificing potential performance for robustness. For multiple measurements, synaptic distributions are broad, approximating better-performing continuous synapses. This observation allows us to choose design principles depending on the desired performance and the device’s operational speed and energy cost. We verify performance on physical hardware, showing it is comparable to a standard neural network.