Energy Load Profile Research Articles

Performance analysis of a solar-assisted ground source heat pump with a single vertical U-tube ground heat exchanger

Owing to higher heat loads than cooling loads in cold climates, the performance of ground source heat pumps (GSHPs) can significantly degrade over time, leading to eventual system failure for systems not properly sized or when there is insufficient regeneration. Solar-assisted ground source heat pump (SAGSHP) systems are receiving increasing interest as a means of enhancing heating performance and maintaining ground thermal balance in cold regions. However, a critical gap remains in the literature: no study has comprehensively analyzed configurations of SAGSHP systems to enhance their overall performance considering long-term performance and realistic operating conditions. For this purpose, a comprehensive investigation of the long-term performance of single U-tube and double U-tube ground heat exchangers (GHEs) for solar-assisted ground source heat pump systems (SAGSHPs) was carried out and compared with the conventional GSHP. The effect of collector tilt angles (0–90°) on the performance of the single U-tube SAGSHP was also examined. A finite volume computational fluid dynamic model was developed and coupled with an actual building energy load profile and a solar irradiation profile based on Calgary’s weather conditions using a novel custom-coded thermodynamic model. Results show that the yearly average heating coefficient of performance (COP) increases by a factor of 1.21 and 1.18 for the single U-tube SAGSHP and double U-tube SAGSHP, respectively, when compared with the conventional GSHP. However, the average annual cooling COP reduces by 0.65 for the single U-tube and 0.72 for the double U-tube SAGSHPs. Moreover, considering the effect of tilt angle on the single U-tube SAGSHP, it is possible to improve the cooling COP when the collector tilt angle is 90°, at the expense of lower heating COP. However, the highest heating COP and annual energy savings are achieved with a tilt angle of 45°. This study provides practical insight into SAGSHP performance enhancements in cold climates.

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.

Adept Domestic Energy Load Profile Development Using Computational Intelligence‐Based Modelling

Most studies undertaken on energy usage in buildings have shown that energy utilization is widely influenced by occupancy presence and occupants’ activities relative to the indoor environment, which may be widely dependent on weather conditions and user behaviors. However, the core drawback that has negated the proficient estimation of energy is the modelling of occupant behavior relative to energy use. Occupants’ behavior is a complex phenomenon and has a dynamic nature influenced by numerous internal, individual, and circumstantial factors. This research proposes a computational intelligence‐based model for household electricity usage profile development as impacted by core input variables—household activities, household financial status, and occupancy presence. The incorporation of these variables and their adaptiveness is expected to address and resolve unpredictability or nonlinearity concerns, thus allowing for adept energy usage estimation. The model addresses issues unresolved in many other studies, such as occupancy determination (deduction) and the impact on energy consumption. The performance precision of this approach has been demonstrated by trend series analysis, demand analysis, and correlation analysis. Based on the performance indicators including mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE), the model has shown proficient predictive output with respect to the metered (actual) energy usage data. The proposed model, compared to actual data, showed that average MAPE values for the respective day standard, morning peak, and night peak demand period (TOUs) are 2.8%, 1.88%, and 0.31% for all income groups, respectively. The aptitude to improve on energy prediction and evaluation accuracy, especially in these periods, makes it a highly suited tool for demand‐side management, power generation, and distribution planning activity. This will translate into power system reliability, reduce operation cost (lowest cost), and reduce greenhouse emissions (environmental pollution), thereby cumulating into sustainable cities.

Validation of the Energy Demand Load Profile Estimator “PROFet” for Trondheim Non-residential Buildings

Accurate long-term forecasts of aggregate energy load profiles are crucial for effective energy system planning at regional and national scales. This study aims to validate PROFet, a flexible load profile modeling tool. PROFet forecasts weather-dependent heating and electrical load profiles at an hourly resolution for both residential and non-residential buildings connected to district heating systems. Given that the tool’s accuracy for residential buildings has been demonstrated in previous studies, further validation is needed for non-residential buildings. To achieve this, our study evaluates PROFet’s extrapolation performance using an out-of-sample test dataset from Trondheim municipality. The results show that PROFet accurately forecasts the hourly heating load during the space-heating season but is less accurate during periods when domestic hot water needs dominate. The tool exhibits nearly identical performance across the three cases, except for efficient kindergartens, where the estimation accuracy is lower.

Analysis and Modeling of Residential Energy Consumption Profiles Using Device-Level Data: A Case Study of Homes Located in Santiago de Chile

The advancement of technology has significantly improved energy measurement systems. Recent investment in smart meters has enabled companies and researchers to access data with the highest possible temporal disaggregation, on a minute-by-minute basis. This research aimed to obtain data with the highest possible temporal and spatial disaggregation. This was achieved through a process of energy consumption measurements for six devices within seven houses, located in different communes (counties) of the Metropolitan Region of Chile. From this process, a data panel of energy consumption of six devices was constructed for each household, observed in two temporal windows: one quarterly (750,000+ observations) and another semi-annual (1,500,000+ observations). By applying a panel data econometric model with fixed effects, calendar-temporal patterns that help explain energy consumption in each of these two windows have been studied, obtaining explanations of over 80% in some cases, and very low in others. Sensitivity analyses show that the results are robust in a short-term temporal horizon and provide a practical methodology for analyzing energy consumption determinants and load profiles with panel data. Moreover, to the authors’ knowledge, these are the first results obtained with data from Chile. Therefore, the findings provide key information for the planning of production, design of energy market mechanisms, tariff regulation, and other relevant energy policies, both at local and global levels.

Local energy management: A base model for the optimization of virtual economic units

As non-renewable resources are limited and the overall CO2 emissions need to be reduced drastically, there is an increasing need in making the energy supply more sustainable. This leads to a needed change in the traditional energy system, in which decentralized local energy resources must be better integrated. In order to face the challenges for a future energy management, the consideration of the domestic level is gaining more attention. In this paper, we aim to get insights into the potential value of local cooperations and focus on economic and control issues as to whether and how the domestic level can participate in upcoming solutions. We introduce a possible setup for a virtual economic unit representing a hybrid cooperation of domestic energy participants, whereby the objective of this cooperation is to realize the maximum possible savings for the community with specified individual contracts between the participants and assuming that participants continue to have additional contracts with their energy service provider. We propose a deterministic linear optimization model for determining optimal energy load profiles of the participants with the external suppliers and energy exchange between participants in a virtual economic unit. To efficiently solve this model and get an exact assignment of supply and demand, we present a maximum saving flow algorithm taking into account the underlying bipartite structure of this problem. The solution achieved is specified as a peer-to-peer allocation between the participants involved and provides insights into the aspects that determine the concrete assignment. It also has the advantage of leading to a robust solution within the collaboration case studies for a basic set-up demonstrate the impact of this approach on the economic potential of aggregating local generation and demand at the same time.

Heating electrification in cold climates: Invest in grid flexibility

One strategy to significantly reduce greenhouse gas emissions in end-use sectors such as building heat is to switch from fossil fuels to electricity. To date, electricity consumption follows a regular and predictable pattern; however, electrifying building heat, which is driven by ambient temperature, causes this pattern to change. Previous work has not fully addressed the implications of building heat electrification on the timing and magnitude of peak demands and on flexibility requirements of the electricity grid. In this work, a high-resolution heating demand model, featuring building stock turnover forecasts, is developed, and applied to British Columbia. The study determines energy, capacity, and flexibility requirements by computing regional energy load profiles for space and water heat. Six scenarios were developed to examine the decarbonization potential in the residential and commercial building sector, considering increased heat pump adoption, building envelope energy efficiency improvements, and the implementation of building energy codes. With an emphasis on building stock improvements, 90% heat pump penetration results in only 6% increase in electrical energy even with an annual population growth rate of 1.1%; however, this scenario leads to a 37% increase in peak electricity demand. Over the time-period considered, building energy codes and retrofit rates contribute relatively little to achieving net-zero emissions in the buildings sector, while building heat electrification has significant impacts on future flexibility requirements of the electricity grid.

Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets

The scale-up of urban electric vehicle (EV) fleets, driven by environmental benefits, is resulting in surging aggregate energy demands that may reshape a city's power supply. This paper establishes an integrated data-driven assessment model for investigating the energy use (kWh) patterns and charging load (kW) profiles of urban-scale EV fleets. To this end, urban EV operating and operational datasets are linked with climate data and vehicle specifications. Four vehicle fleet types are distinguished: private, taxi, rental, and business fleets. Statistical models regarding distribution analysis, spectrum analysis, and identical distribution tests are employed to analyze the patterns of driving distances, energy consumption, and shares of active charging EVs. The minute-level changes in charging EV numbers and aggregate charging power are examined to reflect the grid load impact. The results show that private light-duty EVs in Beijing consume an average of 9.1 kWh/day, with more charging activities on Fridays. The primary load peaks of light-duty EVs in Beijing usually occur between 11 p.m. and 1 a.m., attributable chiefly to the private fleet's midnight peak load estimated at 28 % of the total daily charging private EV count multiplied by 5.5 kW/EV. Secondary peaks occur between 8 a.m. and 10 a.m. on weekdays for private fleets, and at 4 p.m. for public fleets. Our work can be extensively used for analyses on transport emissions, urban power supply, infrastructure build-ups, and policymaking.

Deep neural network with empirical mode decomposition and Bayesian optimisation for residential load forecasting

In the context of a resilient energy system, accurate residential load forecasting has become a non-trivial requirement for ensuring effective management and planning strategy/policy development. Due to the highly stochastic nature of energy load profiles, it is difficult to predict accurately, and usually, predictions are error-prone. This paper explores the potential of Empirical Mode Decomposition (EMD) in simplifying the dynamics of complex demand profiles. The simplified components are then embedded within a deep learning model, specifically Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM), to forecast short-term residential loads. The novel modelling framework integrates Bayesian optimisation strategy, feature decomposition technique, feature engineering phase, and percentile-based bias correction algorithm to enhance model accuracy. The model is developed using a case-study residential dwelling located in Fintry (Scotland), and the model performance is assessed over four forecast horizons. The overall efficiency of framework is also investigated for three algorithms: random forest, gradient boosting decision trees (GBDT), and an LSTM network. While EMD and feature engineering were found to greatly improve prediction accuracy, the number of IMFs used was shown to significantly impact the model’s performance and computational complexity. The model was tested on two further case studies from Fintry.

Development of the virtual battery concept in the paper industry: Applying a dynamic life cycle assessment approach

Energy-intensive industries face the challenge of reducing carbon emissions while remaining competitive. Key measures include fossil fuel substitution, energy efficiency, and integration of renewable energy sources, but their fluctuating production profile makes them difficult to integrate and require industries to adapt their current consumption and production patterns to become more flexible.In this study, the virtual battery concept (VBC), exemplarily demonstrated for a specific site in the paper industry, is introduced and evaluated from an environmental, energetic, and techno-economic perspective. A mathematical optimization model of the class mixed integer linear programming (MILP) was applied to express the industrial site as VBC and derive the operational data basis for the subsequent life cycle assessment (LCA). A dynamic LCA approach is presented to allow the consideration and assessment of the temporal behavior of energy load profiles and their associated environmental implications.The results showed that compared to a static LCA, the dynamic approach leads up to 42 % lower greenhouse gas (GHG) emissions for 1 MJ of public electricity. The global warming potential (GWP) of the total energy supply chain was reduced by 40 % for electricity and 8 % for heat, respectively. By means of a scenario analysis, the GWP to produce one ton of paper was reduced up to 33 % compared to the business-as-usual (BAU) case to 672 kgCO2eq.r in the best case. However, the total primary energy demand (PED) increased by 34 %, but the fossil PED was reduced by 32 % and the renewable PED increased by 157 %. The renewable PED share covered up to 67 % of the total PED. The techno-economic analysis revealed total annual cost savings of up to 44 % to 64.6 € per ton of paper. Environmental costs were estimated to range from 79.6 to 88.9 € per ton of paper.The VBC is considered a promising approach to utilize regional renewable excess electricity effectively, reduce fossil-based energy generation, increase grid stability, and to avoid costly grid infrastructure investments in future. In principle, the VBC is site-independent and replicable to other industries but needs to be evaluated site specifically according to certain process characteristics and requirements.

Coordinated smart home energy sharing with a centralized neighbourhood energy management

This paper presents a centralized neighbourhood energy management with coordinated smart home energy-sharing model for neighbourhood smart homes, which are integrated with in-house renewable energy resources and energy storage systems. The main aim of this neighbourhood energy management model is to minimize the aggregated electricity bill of all smart home consumers in the neighbourhood area by increasing the utilization of local renewable energy generation. The effective combination of time of use pricing, feed-in-tariff scheme and fixed incentive method is considered in this model to encourage the more electricity transactions among the neighbourhood smart homes. A group of ten smart homes with different level of renewable energy integration and load profiles is considered as a test system to determine the performance of the proposed neighbourhood smart home energy management model. The simulation results show that the centralized neighbourhood coordinated smart home energy management model can provide significant economic benefits to all smart home consumers in the neighbourhood area, when compared to the without neighbourhood power-sharing case. From the results, it is noted that all smart homes together get 56.23% of cost savings in a day with the proposed model when compared to the baseline model. Finally, an economic analysis has also done in this paper to investigate the capital investment benefits of all smart home consumers in neighbourhood area. From the economic analysis, it is clearly noticed that with the proposed model the annual profits of all smart homes (SHs) in the neighbourhood area are improved by 54.48% for SH1, 59.88% for SH2, 85.03% for SH3, 41.55% for SH4, 30.68% for SH5, and 34.70% for SH6 respectively when compared to without neighbourhood energy sharing concept.

Corrigendum to “ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles” [Energy Build. 265 (2022) 112096

Corrigendum to “ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles” [Energy Build. 265 (2022) 112096

Economic analysis of integrating photovoltaics and battery energy storage system in an office building

The concept of ‘Active Building’ refers to any building, such as factories, offices, homes, and other structures in the built environment, which are equipped to conserve, generate, store, and release energy in the UK. One such Active Building was built at Swansea University in the UK in 2018 and is currently used as a small office including well fare facilities. The objective of this study is to analyse the economic performance of an Active Building, incorporating building-integrated photovoltaics (BIPV) and lithium-ion (Li-ion) batteries with real building operational profiles and metered energy load profiles. The cost covers the capital cost of 22 kWp BIPV and 110 kWh Li-ion battery, and electricity cost from the electric grid with two types of time of use electricity tariffs - South Wales (SW) time of use tariff and Red, Amber, and Green (RAG) rates, and the potential carbon cost of electricity supplied from the Wales electric grid and generated from the Active Building. Four battery operational strategies are designed, and battery status of charge and electricity flow are monitored. The analysis is undertaken on the BIPV units with or without Li-ion batteries under various scenarios. The results show that the investment of BIPV units without Li-ion batteries can make a profit within the lifetime of BIPV in the current electricity market. However, the current Li-ion battery storage does not compensate for its capital cost from the reduced economic value obtained from the BIPV system when the electricity is curtailed. Even though the current economic analysis of the Active Building installing BIPV and battery is not convincing at the current capital price, this combination can enable the Active Building to be independent of the electric grid and to balance electricity demand and generation itself. The analysis will demonstrate the market conditions required to make these operational benefits cost-effective.

Energy management and load profile optimisation of 10 kWh BESS integrated into a Smart Polygeneration Grid subnetwork

Smart Polygeneration Grids integrate different prime movers, such as traditional generators, renewable energy sources and energy storage systems to locally supply electrical and thermal power to achieve high conversion efficiencies and increase self-consumption. Integrating different energy systems poses some challenges on the plant Energy Management Systems (EMS), which must accommodate different operational requirements while following the electrical and thermal loads. Battery Energy Storage Systems (BESSs) can provide additional flexibility to the system. This paper intends to evaluate the impact of integrating a Ni-Zn-based BESS into an existing cogeneration plant through a dedicated sensitivity analysis over the operative characteristics of the BESS itself (maximum power and capacity). The IES LAB of the Savona’s Campus already contains different energy systems: a cogenerative micro gas turbine, a heat-pump, solar thermal panels and two thermal energy storage systems that provide electricity and thermal power to the Smart Polygeneration Grid of the Campus. A new developed energy scheduler accommodates the integration of the new battery and meets the electrical and thermal demands. The aim is to demonstrate that integrating the BESS provides additional benefits in the system management and can reduce fuel usage and OPEX.

Transition to a resilient and sustainable energy supply system: Decision support for manufacturing companies

Recent developments show that not only material supply chains but also energy supply systems can be hit by strong disruptive forces. This brings new challenges and highlights the importance of a resilient energy supply to the manufacturing industry. These challenges can be solved by implementation of technical solutions and a change of energy supply system designs. However, manifold options available to a company require a methodological approach to rapidly identify the potentially most suitable variants under individual circumstances for a specific factory.This paper introduces a simplified methodology for the first planning steps in the transition towards a resilient energy supply system of production sites. For this purpose, relevant resilience factors are identified and selected for energy supply systems of a factory. With a structured procedure, different technical system combinations are created and evaluated in respect to implementation effort and their potential impact on the resilience factors. Simultaneously, different disruptive scenarios are ranked based on likelihood and expected severity. The impact of these combinations on the energy load profile of the factory is evaluated and the results are presented based on the suitability of each energy supply system alternative in respect to the different disruptive scenarios. This provides information on the potentially most favorable options to the relevant stakeholders for further implementation.To demonstrate the applicability of the developed methodology, a case study on a battery cell factory is presented. The production of batteries is a fast-growing industry with a high relevance for the transition towards a sustainable economy while the battery production itself requires high amounts of electricity and heat at different temperature levels. Alternative supply scenarios besides the unidirectional consumption of fossil fuels are needed in in terms of sustainability and to increase resilience.

TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System.

This paper describes an electricity technical/nontechnical loss detection method capable of loss type identification, classification, and location. Several technologies are implemented to obtain that goal: (i) an architecture of three generative cooperative AI modules and two additional non-cooperative AI modules for data knowledge sharing is proposed, (ii) new expert consumption-based knowledge of feature collaboration of the entire consumption data are embedded as features in an AI classification algorithm, and (iii) an anomaly pooling mechanism that enables one-to-one mapping of signatures to loss types is proposed. A major objective of the paper is an explanation of how an exact loss type to signature mapping is obtained simply and rapidly, (iv) the role of the reactive energy load profile for enhancing signatures for loss types is exemplified, (v) a mathematical demonstration of the quantitative relationship between the features space to algorithm performance is obtained generically for any algorithm, and (vi) a theory of “generative cooperative modules” for technical/nontechnical loss detection is located and mapped to the presented system. The system is shown to enable high-accuracy technical/nontechnical loss detection, especially differentiated from other grid anomalies that certainly exist in field conditions and are not tagged in the universal datasets. The “pooling” architecture algorithm identifies all other loss types, and a robotic process automation module obtains loss type localization. The system feeds from the entire smart metering data, not only the energy load profile. Other solutions, such as a stand-alone algorithm, have difficulty in obtaining low false positive in field conditions. The work is tested experimentally to demonstrate the matching of experiment and theory.

Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates

The United Arab Emirates is moving towards the use of renewable energy for many reasons, including the country’s high energy consumption, unstable oil prices, and increasing carbon dioxide emissions. The usage of electric vehicles can improve public health and reduce emissions that contribute to climate change. Thus, the usage of renewable energy resources to meet the demands of electric vehicles is the major challenge influencing the development of an optimal smart system that can satisfy energy requirements, enhance sustainability and reduce negative environmental impacts. The objective of this study was to examine different configurations of hybrid renewable energy systems for electric vehicle charging in Abu Dhabi city, UAE. A comprehensive study was conducted to investigate previous electric vehicle charging approaches and formulate the problem accordingly. Subsequently, methods for acquiring data with respect to the energy input and load profiles were determined, and a techno-economic analysis was performed using Hybrid Optimization of Multiple Energy Resources (HOMER) software. The results demonstrated that the optimal electric vehicle charging model comprising solar photovoltaics, wind turbines, batteries and a distribution grid was superior to the other studied configurations from the technical, economic and environmental perspectives. An optimal model could produce excess electricity of 22,006 kWh/year with an energy cost of 0.06743 USD/kWh. Furthermore, the proposed battery–grid–solar photovoltaics–wind turbine system had the highest renewable penetration and thus reduced carbon dioxide emissions by 384 tons/year. The results also indicated that the carbon credits associated with this system could result in savings of 8786.8 USD/year. This study provides new guidelines and identifies the best indicators for electric vehicle charging systems that will positively influence the trend in carbon dioxide emissions and achieve sustainable electricity generation. This study also provides a valid financial assessment for investors looking to encourage the use of renewable energy.

Towards sustainable net-zero districts using the extended exergy accounting concept

The net-zero energy concept has attracted increasing attention to facilitate the development of sustainable built environments. Despite promising progress, there is no comprehensive approach that simultaneously considers all technical, economic, environmental, and social parameters. The present study uses the extended exergy accounting concept in the net-zero energy framework to cope with the drawbacks of the existing net-zero definitions. This method weighs all energetic and non-energetic inputs in a single consolidated exergy term, thus making an unbiased comparison of various options possible. The proposed approach is applied to a district in central Iran, and its sensitivity and applicability are compared with the net-zero source energy and exergy methods. The new approach reasonably responds to changes in the energy network and renewable energy system parameters. In the baseline conditions, the new method provides the highest self-consumption index (51.8%) in the investigated case study. Although the net-zero exergy approach generates more renewable energy (two times) than the presented approach, it increases the self-sufficiency index by only 3%. The new approach accounts for the energy load profile while setting up local renewable energy production based on energy network and renewable system efficiency. The developed approach appears to be reliable for developing sustainable net-zero systems.

(Invited, Digital Presentation) Nanostructured Thin Film (NSTF) Iridium Catalyst Powder for Proton Exchange Membrane Water Electrolyzers

Proton exchange membrane water electrolyzers (PEMWEs) are electrochemical devices which generate hydrogen (H2) gas from water and electrical energy feedstocks. PEMWEs produce H2 renewably and carbon-free when the electricity is from renewable sources, and are a pathway to enable deep decarbonization across multiple industrial and energy sectors[1]. However, commercial deployment of PEMWEs is currently limited to megawatt-scale due to relatively higher H2 production costs and capital costs than hydrocarbon reforming [2]. The higher costs are due in part to the use of significant quantities of expensive materials (Pt and Ir electrocatalysts and perfluorinated ionomers), insufficient operating performance and durability, and high manufacturing costs. Additionally, commercial PEMWEs additionally use high Ir loadings [3] for the oxygen evolution reaction (OER), and the limited abundance of Ir [4] may limit PEMWE annual deployment of those technologies to gigawatt (GW) scale.3M Nanostructured Thin Film (NSTF) PEMWE OER powder catalysts and electrodes are a unique approach to address the cost and Ir utilization barriers noted above. NSTF catalysts [5] are comprised of nm-scale catalyst metal thin films on a high aspect ratio inert support (Fig. A). NSTF Ir OER catalysts enable high efficiency and high durability due to high OER mass activity and intrinsic resistance to dissolution, imparted by the unique agglomerated thin film catalyst structure. NSTF OER electrodes [6] consist of a dispersed matrix of NSTF catalyst powder particles within a perfluorosulfonic acid (PFSA) ionomer binder (Fig. B), which have high catalyst utilization due to the high electronic conductivity of the primary catalyst particles.One of the key challenges associated with development of OER catalysts and electrodes is the lack of qualified accelerated stress tests (ASTs) to enable rapid assessments of durability under conditions relevant for end-use. The challenge is in part magnified by the long lifetime requirements of 80,000 hours and low required decay rates of single microvolts per hour, which traditionally has required long testing times and multiple replicates to obtain needed statistical significance. Additionally, evaluations of durability have often occurred under steady state testing with fixed current densities, which do not reflect anticipated use profiles when integrated with renewables such as wind and solar with significant power production variability over time. Lastly, operation at increased stack power densities is considered a key strategy to reduce stack capital costs and Ir requirements on a gram per kW basis.In this paper, we will report recent work on our durability assessment of NSTF OER powder catalysts and electrodes under aggressive testing protocols with low catalyst loadings relevant for PEM electrolyzers at large scale. Assessments included steady state durability tests, an accelerated stress test, and a protocol intended to simulate integration with a wind variable renewable energy (VRE) load profile. An example of results from the wind VRE protocol are summarized in Figs. C and D. The wind VRE protocol generated by Alia et al. [7] was modified from voltage control to current control and the maximum current density was scaled to 4.5A/cm2. The protocol was applied to a 3M laboratory CCM comprising a 0.20 mg/cm2 of 78wt% Ir/NSTF powder catalyst OER electrode, 0.09 mg/cm2 of 78wt% Pt/NSTF powder hydrogen evolution reaction (HER) electrode, and a 100 micron thick PEM (800EW 3M PFSA). After 500 hours of the wind VRE protocol, the cell performance was essentially unchanged (1mV voltage decrease at 2A/cm2). S. Dept. of Energy “H2@Scale”, https://www.energy.gov/eere/fuelcells/h2scale.S. Dept. of Energy H2USA Model, Current Forecourt Hydrogen Production v. 3.101.Ayers et al., Catalysis Today 262 121-132 (2016).Babic et al., Electrochem. Soc. 164 F387 (2017).Debe et al., ECS Trans. 45(2) 47-68 (2012).Steinbach et al., 2019 U.S. DOE Annual Merit Review, Project ta026.Alia et al., Electrochem. Soc. 166 F1164 (2019). Figure 1

INTERNET OF THINGS BASED ZIGBEE SNIFFER FOR SMART AND SECURE HOME

The aim of this research is to determine the usage of energy or power with high spectrum allocation in ZigBee Protocol with the help of clustering in IoT. This research starts presenting an overview of the broadband network energy sector and the challenges that are facing. It is observed a change on the energy policies promoting the energy efficiency, encouraging an active role of the consumer, instructing them about the importance of the consumer behavior and protecting consumer rights. Electricity is gaining room as energy source; its share will keep increasing constantly in the following decades. The objective behind this energy consumption segmentation is to be able to provide personalized recommendations to each group in order to reduce their energy consumption and the associated costs, fostering energy efficiency measures and improving the consumer engagement. The desired segmentation is obtained by an iterative process, based on computational clusters calculation (using python programming language) and finalized by a post-clustering analysis applying visualization and statistical data mining technique to detect the energy consumption and reallocate them to a more appropriate group. The K-Means clustering technique was tested and compared, giving the best prediction of accuracy 98.46% for all energy load profiles with high spectrum of 100GHz. The solution from the K-Means clustering is the one that better adapts to the segmentation sought, which is used as the base of the post-clustering stage to obtain the final energy consumption segmentation. Most of these methodologies use the absolute values in 100 kWh, as they were more focused on identify the users with higher energy savings potential. In this case, it allows personalizing energy savings recommendations according to the specific characteristics of ZigBee protocol, improving the consumer experience by being able to provide the adequate advice at the appropriate time, facts that increase the effectiveness of the energy efficiency advises’ service for future ZigBee protocol.