Peak Load Research Articles

Analysis of vertical loading forces on three different sagittal split ramus osteotomy modifications.

The study aimed to compare and analyze the in vitro mechanical behavior of vertical load on three different sagittal split osteotomy designs proposed by Epker, Wolford, and Wyatt, focusing on the implications of each design on mandibular stability. Synthetic polyurethane hemi-mandibular models were used to replicate the osteotomies according to the designs suggested by Epker, Wolford, and Wyatt. Each model group was subjected to linear vertical loading until system failure, with peak load and deformation recorded. The study utilized a controlled sample preparation and loading test to ensure standardization across all groups. Analysis of variance (ANOVA) and the Tukey test were applied to compare the mechanical responses among the different osteotomy designs. The findings indicated no significant difference in displacement and vertical loading resistance between Groups 1 and 2; however, differences were found in Group 3 (Wyatt), where increased mandibular fragility was observed when screws were placed in thinner bone areas. Statistical analysis showed that the modifications in the osteotomy design led to significant differences in mechanical behavior, particularly in Group 3, highlighting the importance of bone thickness and osteotomy technique on postoperative early stability and mechanical stress distribution. The study concludes that the choice of sagittal split osteotomy design significantly impacts the mechanical behavior under vertical loading, with particular emphasis on the importance of bone thickness at fixation points and the technique used. The findings suggest a preference for the modification proposed in group 1 and 2 in cases where increased mandibular stability and minimized postoperative complications are desired.

Research on interfacial toughness of laser‐induced graphene and short Kevlar fibers aramid fiber/foam aluminum sandwich structure

AbstractThe interface performance of the aramid fiber/foam aluminum sandwich structure is poor due to the lack of fiber connections between the core and face sheets, negatively impacting overall load‐bearing capacity. This study investigates the effects of laser‐induced graphene (LIG) and short Kevlar fibers on the interfacial toughness of aramid fiber/foam aluminum sandwich panels, aiming to enhance the interfacial bonding capability of the structure. Results demonstrate that the synergistic toughening effect of LIG and short Kevlar fibers significantly improves the interlaminar toughness, with a 134.90% increase in peak load and a 161.64% increase in energy absorption during the three‐point bending tests. Scanning electron microscopy (SEM) analysis reveals the toughening mechanism: the micro‐porous structure of LIG provides effective adhesion points for the short Kevlar fibers, creating a stable fiber bridging structure that mitigates interface delamination. Furthermore, optimization of the surface density of the short Kevlar fibers for the LIG‐coated aramid fiber/aluminum foam sandwich panels shows a trend of increasing toughness followed by a decline with increasing surface density, with optimal interfacial performance achieved at a surface density of 6 g/m2. These findings suggest that the combined approach of LIG and short Kevlar fibers is an effective strategy for enhancing the interfacial toughness of aramid fiber/foam aluminum sandwich panels, with promising applications in composite materials.Highlights LIG‐Kevlar fibers improve toughness & energy absorption in sandwich panels. The optimal short Kevlar fiber density for interfacial performance is 6 g/m2. LIG‐Kevlar forms a “bridging structure” to prevent delamination.

Study on the Axial Compressive Behavior of Steel Fiber Reinforced Concrete Confined with High-Strength Rectangular Spiral Stirrup

Monotonic axial compression tests were carried out on 16 steel fiber-reinforced concrete (SFRC) columns confined by rectangular spiral stirrups. The impacts of stirrup spacing, stirrup strength, concrete strength, and cross-sectional aspect ratio on the peak load, ductility, and failure mode of these columns were analyzed. The test results demonstrate that steel fibers significantly mitigate the spalling of the concrete column’s protective layer through their bridging effect. Small spacing and high-strength spiral stirrups effectively confine the core concrete, enhancing the bearing capacity and ductility of concrete columns. Concrete strength exhibits a positive correlation with the confinement effect. However, as concrete strength increases, the rate of improvement in the confinement effect decreases. At peak compressive stress, the high-strength stirrup may not reach its yield state. Based on the test results, a method for calculating stirrup stress under the peak stress of confined concrete is proposed. A “coupling restraint coefficient” is proposed, and a constitutive model for HRSS confined steel fiber reinforced concrete is developed, considering the coupled effect of effective confinement forces in different directions. Comparative analysis shows that the constitutive model established in this paper agrees well with the experimental results and demonstrates good applicability.

Load peak intensity in pre-season training sessions and official competitions in professional soccer

This single case study compares load peak intensity (LPI) in pre-season training sessions and official matches in professional soccer. This aims to answer the question whether the pre-season training sessions of one selected team adequately prepared for the peak demands of the season. Therefore, spatiotemporal data of 8 professional male soccer players was collected using a radio-based local positioning system in training (KINEXON) and a video-based tracking system in Bundesliga competitions (TRACAB). Load peaks were determined based on the distance covered (DC) during Acceleration, Deceleration and Sprinting phases. For each parameter, our algorithm quantifies the LPI by using the maximum rolling average value for time intervals of 1, 3, and 5 min within a training session or a match. The results on team level indicate that LPI for Acceleration was significantly higher in pre-season training sessions compared to matches (for 3-min interval: DCTR = 22.59 m ± 3.06 m, DCMA = 18.38 m ± 3.08 m, d = 1.97, p < 0.01). In contrast, LPI regarding Deceleration was significantly lower in pre-season training compared to matches (for 3-min interval: DCTR = 20.8 m ± 2.65 m, DCMA = 23.15 m ± 3.17 m, d = −1.29, p < 0.01). This suggests that exercises focused more on acceleration situations and less on deceleration situations. However, analysis on an individual level shows a considerably high variability of load peak intensity between players during both periods. We conclude that individual load monitoring is an essential component of training management. Practitioners are especially advised to carefully examine the worst-case scenarios of Deceleration during pre-season to avoid falling short on load requirements and adequately prepare players for match demands.

Short-Term Building Electrical Load Prediction by Peak Data Clustering and Transfer Learning Strategy

With the gradual penetration of new energy generation and storage to the building side, the short-term prediction of building power demand plays an increasingly important role in peak demand response and energy supply/demand balance. The low occurring frequency of peak electrical loads in buildings leads to insufficient data sampling for model training, which is currently an important factor affecting the performance of short-term electrical load prediction. To address this issue, by using peak data clustering and knowledge transfer from similar buildings, a short-term electrical load forecasting method is proposed. First, a building’s electrical peak loads are clustered through peak/valley data analysis and K-nearest neighbors categorization method, thereby addressing the challenge of data clustering in data-sparse scenarios. Second, for peak/valley data clusters, an instance-based transfer learning (IBTL) strategy is used to transfer similar data from multi-source domains to enhance the target prediction’s accuracy. During the process, a two-stage similar data selection strategy is applied based on Wasserstein distance and locality sensitive hashing. An IBTL strategy, iTrAdaboost-Elman, is designed to construct the predictive model. The performance of proposed method is validated on a public dataset. Results show that the data clustering and transfer learning method reduces the error by 49.22% (MAE) compared to the Elman model. Compared to the same transfer learning model without data clustering, the proposed approach also achieves higher prediction accuracy (1.96% vs. 2.63%, MAPE). The proposed method is also applied to forecast hourly/daily power demands of two real campus buildings in the USA and China, respectively. The effects of data clustering and knowledge transfer are both analyzed and compared in detail.

Direct Load Control Strategy of Centralized Chiller Plants for Emergency Demand Response: A Field Experiment

As the penetration rate of renewable energy in the power grid increases, the imbalance between power supply and demand has become one of the key issues. Buildings and their heating, ventilation, and air conditioning (HVAC) systems are considered excellent flexible demand response (DR) resources that can reduce peak loads to alleviate operational pressures on the power grid. Centralized chiller plants are regarded as flexible resources with large capacity and rapid adjustability. The direct load control of chiller plants can respond to the power grid within minutes, making them highly suitable for participation in emergency DR. However, existing studies are generally based on simulations and lack experimental research in actual large-scale buildings to demonstrate the effectiveness of this method and provide related lessons learned. This study conducted field experiments on a centralized chiller plant within an industrial building in Guangdong, China. The results indicate that the strategy of shutting down chiller plants is an effective DR measure. It can complete the load reduction process within 15 min, rapidly decreasing the system power by 380~459 kW, with a maximum duration of up to 50 min, without significantly affecting the thermal comfort of indoor occupants. Additionally, the impact of existing control logic on the participation of chiller plants in the DR process is also discussed.

Patients with Achilles tendinopathy use compensation strategies to reduce tendon load during rehabilitation exercises

BackgroundThis study aimed to determine differences in the Achilles tendon loading during rehabilitation exercises for Achilles tendinopathy and the ranking of these exercises, based on load, in patients with tendinopathy and controls. MethodsSixteen patients with Achilles Tendinopathy (5F & 11 M, 44.1 ± 12.9 yr) and sixteen controls (4F & 12 M, 39.4 ± 15.6 yr) performed rehabilitation exercises while 3D motion and ground reaction forces were measured. Musculoskeletal modeling was used to compute joint kinematics and estimate Achilles tendon load by summing the forces of individual triceps surae muscles. Subsequently, peak Achilles tendon loading, loading impulse, loading rate, loading indexes (a combination of the previous parameters), and joint angles at the time of peak loading were determined and compared between patients and controls. FindingsPatients with tendinopathy exhibited significantly reduced peak Achilles tendon loading compared to controls during the exercises with the highest peak loading: unilateral heel drop with flexed knee (3.66 ± 0.90BW [AT] vs. 4.65 ± 1.10BW [Control], p = 0.003, d = 0.979) and walking (3.37 ± 0.49BW [AT] vs. 3.68 ± 0.33BW [Control], p = 0.044, d = 0.742). Additionally, during the heel drop exercise, patients with tendinopathy showed reduced ankle dorsiflexion and knee flexion. The ranking of exercises by peak loading or loading index was similar for both groups but varied depending on which loading parameter was used to define Achilles tendon loading. InterpretationDuring the highest load-imposing exercises, patients with tendinopathy employ compensatory strategies to reduce the load on their Achilles tendon. Clear instructions and feedback on the patient's performance are crucial as altered exercise execution influences Achilles tendon loading.

Signal response and energy evolution of separated sandstone

This study aims to establish a precise prediction system for strong rockbursts and mine earthquakes caused by high-hard roof fractures. A three-point bending test was conducted on separated sandstone (SS) to analyze deformation field changes under the influence of digital image correlation (DIC) and acoustic emission (AE) signals during rock fracture. Microscopic fracture modes and energy evolution patterns of SS are investigated using scanning electron microscopy and Particle Flow Code in 2 Dimension, revealing the internal mechanisms linking fracture modes, crack classifications, and energy evolution. The results show that the AE signals and DIC deformation fields are highly correlated. Tensile cracks are the primary indicator of damage to SS during the strong contact phase. Complete failure occurs at peak load, driven by a combination of tensile and shear cracks. Increasing the precast crack length (b) or the upper-to-lower rock layer thickness ratio (U/L) enhances tensile failure sensitivity, whereas simultaneous increases reduce it. Macroscopically, this is reflected in variations of AE peak frequency and the primary fracture's peak frequency. Microscopically, it appears as changes in fracture surface roughness and grain fracture morphology. Energetically, this is evident in changes to the ratios of strain energy and slip energy relative to total energy. Additionally, as b and U/L increase, the primary crack location and AE energy density concentration shift progressively from the lower to the upper rock layers. Meanwhile, total input energy decreases with increasing b or as U/L deviates from 1.

Interaction between F/TRC shear strengthening and steel stirrups: Experimental observations on real-scale RC T-beams

The increasing age, degradation, and performance demands on existing structures are driving the construction industry towards the development of high-performance materials for the production of strengthening solutions. When these solutions are used to increase the shear resistance of Reinforced Concrete elements, they tend to exhibit an adverse interaction with the stirrups. An experimental campaign was conducted to investigate this phenomenon on a rather novel material, i.e., Fibre/Textile Reinforced Concrete (F/TRC). The results of this campaign show that there is indeed an interaction which can significantly reduce the contribution of the F/TRC layers, even if the stirrups were past their yielding deformation. Furthermore, it was also observed that the reduction in contribution of the strengthening solutions was accompanied by a reduction of the deformation of the stirrups at the peak load.

Proposal of Peak Load Suppression Measures for EV Quick Charging using Charge Shift and Storage Battery

Proposal of Peak Load Suppression Measures for EV Quick Charging using Charge Shift and Storage Battery

Multi-objective control to schedule therapies for acute viral infections.

Antiviral therapies can yield different outcomes depending on their scheduling: a highly effective drug may produce treatment results ranging from successful to inconsequential, depending on therapeutic timing, dosing intervals, and dosage. The effectiveness of antiviral therapies can be assessed using mathematical models that describe viral spread within a host. In this work, we conduct a study based on the dynamic characterization of a target-cell model to address a multi-objective control problem aimed at designing highly effective and host-customizable antiviral therapies. These therapies involve finite-time antiviral treatments that minimize the viral load peak and the infection final size until infection clearance, while simultaneously reducing the total amount of drug intake as much as possible. Two optimization-based control strategies are proposed: a fixed-dose and a variable-dose approach. The variable-dose strategy achieves superior performance by explicitly considering the system dynamics in the design of the control. Simulation results, based on an identified model for COVID-19 patients treated with Paxlovid, illustrate the potential benefits of the proposed strategies.

Capacity Planning for High-Traffic Events in Cloud Environments

Cloud capacity planning for high-traffic events has become crucial for modern digital businesses facing unprecedented challenges during seasonal sales, product launches, and viral marketing campaigns. Organizations must implement robust strategies to handle sudden traffic surges while maintaining system stability and user experience. Advanced technologies including artificial intelligence, machine learning, and edge computing enable precise demand forecasting, dynamic resource allocation, and real-time monitoring. These capabilities, combined with automated scaling mechanisms and comprehensive security measures, allow businesses to optimize costs while ensuring consistent performance during peak loads. The integration of cloud-native tools and platforms further enhances operational efficiency through predictive analytics and automated resource management, making it possible for organizations to build resilient systems capable of handling extreme traffic variations while maintaining high availability and customer satisfaction.

Event-Driven Serverless Architectures for High-Scale Customer Support: An Empirical Analysis

Serverless computing has emerged as a transformative paradigm for building scalable cloud applications, yet its application in customer support systems remains largely unexplored. This article presents a comprehensive analysis of a cloud-native serverless architecture designed to address the scalability challenges inherent in modern customer support operations. The article proposes an event-driven framework that leverages Function-as-a-Service (FaaS) platforms across major cloud providers, demonstrating significant improvements in resource utilization and cost efficiency compared to traditional microservices architectures. Through empirical evaluation and real-world case studies, the article analyzes the performance characteristics of our proposed architecture under varying workload conditions, with particular attention to cold start latencies and event processing throughput. The findings reveal that serverless architectures, when properly implemented with event orchestration systems like Apache Kafka or AWS EventBridge, can provide superior scalability and cost benefits while maintaining system reliability. The article also identifies key architectural patterns and implementation strategies that organizations can adopt to optimize their customer support infrastructure, particularly during peak load periods.

Real-Time Data Processing in Cloud Computing: Enterprise Implementation Strategies

This article examines the transformative impact of real-time data access enabled by cloud computing across enterprise organizations, focusing on implementation strategies and architectural patterns. Through detailed analysis of industry-leading case studies, this article explores how cloud-based real-time data processing solutions have revolutionized business operations and customer experiences. This article investigates critical technical components including data streaming architectures, IoT integration, geospatial processing, and predictive analytics implementations. This article demonstrates that successful real-time data processing implementations require careful consideration of infrastructure requirements, scalability patterns, and performance optimization strategies. This article provides insights into common challenges such as handling peak loads, ensuring data consistency, and maintaining system reliability, while also examining emerging trends in edge computing and artificial intelligence integration. This article contributes to the growing body of knowledge on enterprise-scale real-time data processing and offers practical guidance for organizations planning similar implementations.

Oxygen pulse during cardiopulmonary exercise testing in cardiac patients as a surrogate of stroke volume: fact or fiction?

Abstract Background According to the Fick equation, oxygen uptake (VO2) equals the product of cardiac output (CO) and arteriovenous oxygen difference (a-vO2Diff). Cardiopulmonary exercise testing (CPET) provides the oxygen pulse (O2pulse) as the ratio of VO2 and heart rate. The O2pulse is often considered a surrogate for stroke volume (SV) during effort, ignoring the AVO2Diff, to assess the hemodynamic response during exercise. Purpose To compare O2pulse and SV measured independently by simultaneous CPET and exercise echocardiography (CPETecho). Methods Observation study of CPETecho exams performed between July 2015 and May 2024. The O2pulse was obtained by CPET at rest, intermediate and peak loads, while the SV was calculated by the ultrasound velocity time integral of the left ventricular outflow tract and the estimated a-vO2Diff by the Fick equation. Variables were compared by one-way ANOVA and Tukey test. Differences in O2pulse and SV between intermediate and peak (ΔO2pulse and ΔSV) were used to analyze the agreement of effort patterns: ‘ascending’ with values increasing from intermediate to peak, or ‘descending’ with the opposite response. The pattern agreement was evaluated by Cohen kappa. A p < 0.05 was considered significant. Results A total of 1,866 exams were included in the study (54.1% males, age: 65.4 ± 13.9 y). The indications for the exams were heart failure with preserved ejection fraction (39.7%), valvular heart diseases (35.5%), investigation of myocardial ischemia (6.3%) and others (18.5%). The VO2 at intermediate level was 66±16% of the peak values. The O2pulse significantly raised from rest to intermediate and peak (4.0±1.9, 9.0±3.3 and 10.9±3.9 mL/beat; p < 0.0001; Figure 1). The SV during rest, intermediate and peak were 70.2±18.1, 84.5±20.9 and 85.1±21.3 mL/beat, respectively, without significant difference between intermediate and peak (p = 0.074). In contrast, the a-vO2Diff progressively increased from rest, intermediate and peak (p < 0.001). Notably, the ΔO2pulse and ΔSV exhibited a very weak correlation (r = 0.11, Figure 2). An ascending pattern was identified in 86.4% when considering ΔO2pulse and in 48% for ΔSV, with specific agreement of 43.4%. Conversely, a descending pattern was identified in 13.6% for ΔO2pulse and in 52.0% for ΔSV, with a specific agreement of 8.9%. The overall agreement was 52.3% (Cohen's kappa: 0.07), indicating almost no agreement between the patterns. Conclusion This study reveals a very low agreement between changes in O2pulse and SV during exercise in cardiac patients. The rise in O2pulse from intermediate to peak exercise was more closely related to the increase in a-vO2Diff whereas SV remained constant. Consequently, in cardiac patients, O2pulse changes cannot be assumed to be a reliable surrogate for SV changes, but rather reflect the combined effects of SV and muscle oxygen extraction, underscoring the importance of CPETecho for assessing exercise hemodynamic trends accurately.

Advanced model predictive control strategy for thermal management in multi-zone buildings with energy storage and dynamic pricing

In this study, we propose a modified model predictive control (MPC) strategy for managing the thermal load in buildings, aimed at creating a fine-tuned balance between indoor thermal comfort and electricity cost reduction. Here, the multi-zone building’s state-space model is employed to dynamically manage energy consumption while preserving occupant comfort. The key contributions of this work include the development of a novel economic MPC strategy tailored for multi-zone heating, ventilation, and air conditioning (HVAC) systems, integrating thermal energy storage to optimise energy usage and occupant comfort. Additionally, we introduce an enhanced multi-objective optimisation framework that transforms the conflicting objectives of energy efficiency and occupant comfort into a single-objective problem for improved computational efficiency. The control strategy also incorporates dynamic electricity pricing, enabling cost-effective operation by shifting energy consumption to lower-cost periods. The proposed control method reduces fluctuations in indoor air temperature, extending the operational life of HVAC system actuators. Beyond reducing costs and consumption, this approach alleviates energy production strain and peak demand on the smart grid. The optimisation process incorporates user-defined temperature preferences for each zone, ensuring tailored comfort conditions. Simulation results show that this method maintains indoor air temperature within the desired comfort range, outperforming traditional methods prone to fluctuations. Furthermore, the proposed MPC strategy effectively shifts the peak load to periods of lower electricity prices, achieving an 18.58% reduction in overall energy costs.

Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy

Recently, electric distribution grids supply not only electric loads but also heating and cooling loads simultaneously to increase the efficiency of the system and reduce the emission of greenhouse gases. An energy management system (EMS) to reduce the combined total expense including environmental damage cost of the combined cooling, heating, and power (CCHP) smart distribution grids in a cooperative framework is proposed in this paper. The entire problem is modelled as a unit commitment interval mixed integer quadratic program (UCIMIQP). The UC is developed to respond to the operation of the electric, heating, and cooling systems and takes into consideration the exchange of energy between these systems. In addition, the demand response (DR) is incorporated with the optimization problem as a decision variable to shave the peak load and reduce the total system cost. The environmental damage is converted to expense, and the entire combined problem is converted to a unified function that is possible to solve in one step, where this is suitable for online operation. Furthermore, a set of realistic constraints is considered to make the approach close to a real scenario. To verify the effectiveness and robustness of the proposed model, the analysis is applied to the distribution grids, which include electrical, heating, and cooling systems, where these systems operated cooperatively. The interaction between these systems makes the operation more flexible and economical. The results show that the total cost is reduced through an exchange in energy between the systems. Additionally, the consideration of the demand response reduces the maximum load and decreases the total cost.

Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management

Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the stable operation of regional clean energy power supply networks. This study focuses on the problem of vehicle charging dispatch to make a breakthrough, deeply analyzes the effect and efficiency of the clean energy grid, and then proposes a series of targeted measures to effectively improve the operational efficiency and reliability of the energy system. The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. Experimental data strongly confirms that the optimization strategy has led to a 15% reduction in peak load on the grid, a 23% increase in the proportion of clean energy consumption, and a 10% reduction in total electricity consumption. For policymakers, these achievements can be used as a guide to help formulate energy policies and build a framework for adapting to the development of new energy. For practitioners, they serve as a guide to energy planning, grid dispatch, and technology research and development to improve effectiveness. The research promotes the growth of green energy, optimizes the energy structure, lays the foundation for a low-carbon and environmentally friendly society, affects the economy, environment, culture, and other fields, and becomes a key force driving sustainable development.

Process Integration and Optimization of the Integrated Energy System Based on Coupled and Complementary “Solar-Thermal Power-Heat Storage”

Within the context of “peak carbon and carbon neutrality”, reducing carbon emissions from coal-fired power plants and increasing the proportion of renewable energy in electricity generation have become critical issues in the transition to renewable energy. Based on the principles of cascaded energy utilization, this paper improves the coupling methodology of an integrated solar thermal and coal-fired power generation system based on existing research. A parabolic trough collector field and a three-tank molten salt thermal energy storage system are connected in series and then in parallel with the outlet of the reheater. ASPEN PLUS V14 and MATLAB R2018b software were used to simulate a steady-state model and numerical model, respectively, so as to study the feasibility of the improved complementary framework in enhancing the peak load capacity of coal-fired units and reducing their carbon emissions. Actual solar radiation data from a specific location in Inner Mongolia were gathered to train a neural network predictive model. Then, the peak-shaving performance of the complementary system in matching load demands under varying hours of thermal energy storage was simulated. The findings demonstrate that, under constant boiler load conditions, optimizing the complementary system with a thermal energy storage duration of 5 h and 50 min results in an energy utilization efficiency of 88.82%, accompanied by a daily reduction in coal consumption by 36.49 tonnes. This indicates that when operated under the improved coupling framework with optimal parameters, the peak regulation capabilities of coal-fired power units can be improved and carbon emission can be reduced.

Can residential energy systems withstand the heat? Quantifying solar photovoltaic and heat pump yields for future New Zealand climate conditions

Abstract We investigate how higher temperatures resulting from climate change impact the energy system. Specifically, we examine the cumulative effects of fluctuating solar photovoltaic (PV) generation performance, heating and cooling demand, and heat pump efficiency on such days.
To achieve this, we used the climate analogue space, which maps a given city’s future climate to an existing one. By employing climate analogues, we can predict the impact of higher temperatures by 2050, transforming Auckland, New Zealand’s climate into one akin to Sydney, Australia. This approach avoids reliance on historical weather data, which many energy system models use. We used this future climate time series as an input to a residential energy system model for Auckland, New Zealand. The residential energy system model simulates solar PV generation output via mapping of experimental data, building thermal characteristics via grey-box resistance-capacitance (RC) modelling, and hourly coefficient of performance (COP) for air source heat pumps (ASHP) via linear regression.
Our findings revealed that a future climate doubles the cooling demand and reduces the heating demand by one-third, with the heat pump demand peak load projected to be 40% higher than current demand. Although solar PV generation experiences a decrease in efficiency of 8%, there is a 40% increase in annual direct usage of ASHP. Despite the high cooling demand, the combined yearly electricity demand for heating and cooling decreased by 6.5% overall, and the system saw a 50% improvement in demand fulfilment. However, the system performance volatility at hotter-than-normal temperatures and the potential for significant energy shortfalls remain concerns. The shift from a predominantly heating to a cooling environment is a critical design condition that should be considered in energy expansion planning and future electrification.
The framework and time series developed in this work can be expanded and applied to other energy system modelling exercises.