Average Utilization Research Articles

Preparation, performance study and application simulation of gypsum-paraffin/EG composite phase change building wallboard

The performances and application parameters of phase change materials (PCM) are crucial for integration with construction in practice. In this paper, composite PCMs (CPCM) were prepared by paraffin/expanded graphite (EG) and applied to phase change gypsum board (PCGB). The composite PCM was characterized by SEM, DSC, leakage ratio and thermal conductivity tests to determine the optimal ratio of EG and paraffin; The thermal storage and release properties of PCGB were experimentally investigated by two building cabin models. The results showed that the CPCM obtained 12 wt% EG exhibited excellent thermal conductivity (2.32 W/mK) and latent heat (103.9 J/g). The PCGB prepared with above CPCM has excellent thermal conductivity (0.3968 W/mK) and latent heat (42.2 J/g). When the EG content exceeded 12%, the thermal properties and leakage ratio of the CPCM stabilized after 200 thermal cycles despite the increase of EG. The phase change cabin exhibited much lower heating and cooling rates than that of the pure gypsum cabin, with a 1.14 °C decrease in peak internal surface temperature and a 3.4 °C decrease in temperature fluctuation. In addition, the room temperature of the established reinforced concrete wall model with PCGB was simulated based on the hourly variation pattern of the winter-summer temperature in Urumqi, China. The results show that in summer, the PCGB with phase change temperature of 29–31 °C and thickness of 20 mm possesses a larger heat storage capacity, with a smaller average internal surface temperature (28.63 °C) and a higher average phase change utilization (61.5%), which can reduce the cooling and heating load of the wall and save energy effectively.

A Simulation for Resource Allocation in a Job Selection Process System

Simulation studies can be carried out in various sectors, including the manufacturing, service, and government sectors. The scope of simulation studies ranges from simple systems to complex systems. This research was conducted on the job selection process system. The simulation method is used to evaluate system performance and make improvements. Based on the analysis of system performance, several improvement strategies were developed for system performance. Strategy selection is based on the low utilization of staffs in the locations. Improvement strategy I and II focus on reducing staff at Registration & Scanning CV location, while improvement strategy III also reduces another staff at the 2nd Interview location. For example, using Strategy III can increase the average utilization of staff at Registration & Scanning CV location to 82.22% and at the 2nd interview location increase to 6.46%. By reducing the number of resources, the company gets cost-savings without increasing the mean time in the job selection system, as can been seen from p-value of ANOVA which is more than 0.05. This simulation study suggests appropriate improvements in this job selection system.

WHO GETS HOSPICE VISITS AT THE END OF LIFE: A LOOK AT MEDICARE'S HOSPICE SERVICE INTENSITY ADD-ON POLICY

Abstract In 2016, the Centers for Medicare & Medicaid Services (CMS) implemented the Service Intensity Add-on (SIA) payment for the Medicare Hospice Benefit. The SIA incentivizes direct patient care by registered nurses and medical social workers during the last seven days of life through additional payments to hospices. We examined 100% of Fee-for-Service Medicare hospice claims in Fiscal Year 2020 to determine whether SIA utilization varies by patient and hospice characteristics. Using hospice level fixed-effects regression, we examine the associations between SIA utilization and various beneficiary and hospice characteristics. Relative to beneficiaries with cancer as a principal diagnosis, we find that other diagnoses are associated with only a slight reduction in SIA utilization (a reduction between 0.6 and 1.1 percentage points - where the average SIA utilization overall is equal to 85.3%). Those identifying as black had a reduction in SIA of 1.3 percentage points compared to those identifying as white. Compared to a patient dying on a Sunday, a patient’s death on Tuesday through Friday was associated with a higher likelihood of SIA (between 9.7 and 11.6 percentage points more likely) and more SIA minutes (between 29.9 minutes and 36 minutes). All reported results were statistically significant at the 0.1% level. Based on these results, we find that SIA utilization varied by certain characteristics. As a result, CMS should continue to monitor rates of SIA utilization to better understand whether any patient groups appear to be underserved at the end of life.

Impact of malaria control interventions on malaria infection and anaemia in low malaria transmission settings: a cross-sectional population-based study in Sudan.

The past two decades were associated with innovation and strengthening of malaria control interventions, which have been increasingly adopted at large scale. Impact evaluations of these interventions were mostly performed in moderate or high malaria transmission areas. This study aimed to evaluate the use and performance of malaria interventions in low transmission areas on malaria infections and anaemia. Data from the 2016 Sudan malaria indicator survey was used. Multi-level logistic regression analysis was used to assess the strength of association between real-life community-level utilization of malaria interventions [diagnosis, artemisinin-based combination therapies (ACTs) and long-lasting insecticidal nets (LLINs)] and the study outcomes: malaria infections and anaemia (both overall and moderate-to-severe anaemia). The study analysis involved 26,469 individuals over 242 clusters. Malaria infection rate was 7.6%, overall anaemia prevalence was 47.5% and moderate-to-severe anaemia prevalence was 4.5%. The average community-level utilization was 31.5% for malaria diagnosis, 29.9% for ACTs and 35.7% for LLINs. The odds of malaria infection was significantly reduced by 14% for each 10% increase in the utilization of malaria diagnosis (adjusted odds ratio (aOR) per 10% utilization 0.86, 95% CI 0.78-0.95, p = 0.004). However, the odds of infection was positively associated with the utilization of LLINs at community-level (aOR per 10% utilization 1.20, 95% CI 1.11-1.29, p < 0.001). No association between malaria infection and utilization of ACTs was identified (aOR per 10% utilization 0.97, 95% CI 0.91-1.04, p = 0.413). None of the interventions was associated with overall anaemia nor moderate-to-severe anaemia. There was strong evidence that utilization of malaria diagnosis at the community level was highly protective against malaria infection. No protective effect was seen for community utilization of ACTs or LLINs. No association was established between any of the interventions and overall anaemia or moderate-to-severe anaemia. This lack of effectiveness could be due to the low utilization of interventions or the low level of malaria transmission in the study area. Identification and response to barriers of access and low utilization of malaria interventions are crucial. It is crucial to ensure that every suspected malaria case is tested in a timely way, notably in low transmission settings.

Simulation Based Analysis of Hierarchical Timed Colored Petri Nets Model of the Restaurant Food Serving Process

The major goal of establishing a restaurant is improved earnings making which can be jeopardized by customers' withdrawals due to lengthy ready lines. The above scenario was studied in this paper using simulation-based analysis of a Hierarchical Timed Coloured Petri Nets (HTCPN) model, which abstracted the serving operation of a renowned restaurant in the Ibadan metropolis. The restaurant was characterized by two or more servers that serially served each Customer from the multiple available servers at the four serving points. One hundred simulation runs were carried out on the HTCPN model using CPN Tools to determine the four (4) parameters,Customers' Average Flow Time (CAFT),Customers' Average Waiting Time (CAWT),Servers' Average Utilization Rate (SAUR) andCustomers' Average Queue Length (CAQL) required in serving 567, 1100, 1103, 770 and 829 customers from Monday to Friday, respectively. The model was validated by carrying out a statistical analysis t-Test between the simulated and the actual customers' flow time andservers' utilization rate at a 5% significant level. The validation results showed no significant differences between the simulated and the actual customers' average flow time andservers' utilization rate at a 5% significance level. Hence, the model can represent the restaurant under consideration.

A Multi-Equipment Task Assignment Model for the Horizontal Stripe Pre-Cut Mining Method

This paper proposes a multi-equipment task assignment model for the horizontal stripe pre-cut mining method to address the problem of cooperative scheduling operation of multi-equipment in underground metal mines under complex constraints. The model is constructed with multiple objectives, including operation time, operational efficiency, equipment utilization rate, and ore grade fluctuation by considering the constraints of time, space, equipment, and processes. The NSGA-III algorithm is used to obtain the solution. The effectiveness of the algorithm is tested based on the actual data from the Chambishi Copper Mine. The results show that the average equipment utilization rate is 51.25%, and the average ore output efficiency is 278.71 tons/hour. The NSGA-III algorithm can quickly generate the optimal multi-equipment task assignment solution. The solution reduces the interference of manual experience and theoretically improves the actual operation of the mine.

Stable and efficient resource management using deep neural network on cloud computing

Resource management autoscaling in a cloud computing service guarantees the high availability and extensibility of applications and services. Horizontal pod autoscaling (HPA) does not affect the executed tasks but also has the disadvantage that it cannot provide immediate scaling. Furthermore, scale down is not possible if excess resources are allocated, because it is difficult to identify the amount of resources required for applications and services; thus resources are wasted. Therefore, this study proposes Proactive Hybrid Pod Autoscaling (ProHPA), which immediately responds to irregular workloads and reduces resource overallocation. ProHPA uses a bidirectional long short-term memory (Bi-LSTM) model applied with an attention mechanism for forecasting future CPU and memory usage that has similar or different patterns. Reducing excessive resource usage with vertical pod autoscaling (ReVPA) adjusts the overallocation of resources within a pod by forecasted resource usage. Lastly, prevention overload with HPA (PoHPA) immediately performs resource scaling by using forecasted resource usage and pod information. When the performance of ProHPA was evaluated, CPU and memory average utilization were improved by 23.39% and 42.52%, respectively, compared with conventional HPA when initial resources were overallocated. In addition, ProHPA did not exhibit overload compared to conventional HPA when resources are insufficiently allocated.

Natural variations and dynamics of macronutrients for 87 tea plant (Camellia sinensis) varieties throughout the growing seasons in Wuhan

The tea plant (Camellia sinensis (L.) O. Kuntze) is the most extensively consumed beverage crop in the world, with steadily increasing consumption over the last decade. Tea plantations are frequently over-fertilized to achieve enhanced growth and yield, which ultimately influences the soil acidification and compaction, resulting in low quality and yield. However, the effect of optimum nutrient concentrations in the leaves on tea yield and quality is poorly understood. In this study, the dynamics of macronutrients nitrogen (N), phosphorus (P) and potassium (K) levels, nutrient use and remobilization efficiencies, and nutrient requirements (NR) were quantified among the diverse tea plant varieties over the entire growing seasons. Leaf samples of two categories, young leaves and mature canopy leaves, were collected from 87 elite tea varieties during the growing seasons to illustrate their biological significance. The results revealed that the highest average N levels were observed in the 2nd spring tea, P levels in summer tea, and K levels in autumn tea in both young and mature leaves. The results further revealed that a higher average nutrient utilization efficiency for nitrogen (NUtE-N) was identified in the summer followed by autumn. The NUtE-P in autumn and the NUtE-K in the 1st spring tea were relatively higher. Nutrient remobilization efficiencies (NRE) (NRE < 0 and NRE > 0) for N, P and K within canopy leaves were calculated, indicating the alternative roles of sources and sinks during the growth period. Moreover, NRE < 0 was almost exclusively found in the spring and summer, whereas NRE > 0 was mainly observed in the autumn. In terms of requirements, N was the most required for optimal tea yield, followed by K, and then P. Seasonally, maximum N, P and K requirements were coincidentally observed in the 2nd spring tea, whereas the minimum were in summer tea, autumn tea and the 1st spring tea, respectively. Based on nutrient utilization, the nutrient requirements of tea plants and the understanding of natural variability in N, P and K dynamics were elucidated to address agronomic, economic and environmental issues.

Integrating Artificial Neural Networks, Simulation and Optimisation Techniques in Ambulance Deployment for Heterogeneous Regions under Stochastic Environment

The paper focuses on the development of a strategy to integrate forecasting using artificial neural networks (ANN), simulation and optimisation techniques for ambulance deployment to predefined locations with heterogeneous demand patterns under stochastic environments. The metropolitan city of Bulawayo was used as a case study with high variability in call inter-arrival rates, response times, service times, and proportions of severity of emergencies by geographical zones covered by sub-stations. These stochastic environments complicate the decision-making process at strategic, tactical and operational level, in pursuit to achieve high levels of equality, efficiency and effectiveness in resource allocation and utilisation. This paper proposes an integrated simulation optimisation methodology that integrates future demand and allows for simultaneous evaluation of operational performances of deployment plans using multiple performance indicators such as average response time, total duration of a call-in system, number of calls in response queue, average queuing time, throughput ratios and ambulance utilisation levels. Increasing the number of ambulances influences the average response time below a certain threshold. Beyond this threshold, no significant changes occur in the performance measures. As the fleet size is increased, the ambulance utilisation levels decreased, hence there is always need to balance resource allocation and capacity utilisation to avoid idleness of essential equipment and human resources. Numerical experiments conducted to align the response time to international standards resulted in reduction in number of ambulances required for optimal deployment. For medical resources such as ambulances, deploying more resources do not always translate to better performance, hence there is need to simultaneously consider multiple performance measures. Decision makers in EMS must seriously consider ways of reducing the response time as it has significant bearing in reducing the required number of ambulances, a critical but scarce resource. Efforts must be directed towards digitisation of switch boards in the call center, training of the paramedics and provision of relevant modern equipment to the response teams as it will go a long way in reducing the pre-trip delay time, chute time and ultimately the response time. Based on the scientific evidence, management could lobby for de-congestion and resurfacing of old and dilapidated roads in order to increase access and speed when responding to emergency calls.

Simulation modeling assessment and improvement of a COVID-19 mass vaccination center operations.

The development of safe and effective vaccines against COVID-19 has been a turning point in the international effort to control this disease. However, vaccine development is only the first phase of the COVID-19 vaccination process. Correct planning of mass vaccination is important for any policy to immunize the population. For this purpose, it is necessary to set up and properly manage mass vaccination centers. This paper presents a discrete event simulation model of a real COVID-19 mass vaccination center located in Sfax, Tunisia. This model was used to evaluate the management of this center through different performance measures. Three person's arrival scenarios were considered and simulated to verify the response of this real vaccination center to arrival variability. A second model was proposed and simulated to improve the performances of the vaccination center. Like the first model, this one underwent the same evaluation process through the three arrivals scenarios. The simulation results show that both models respond well to the arrival's variability. Indeed, most of the arriving persons are vaccinated on time for all the studied scenarios. In addition, both models present moderate average vaccination and waiting times. However, the average utilization rates of operators are modest and need to be improved. Furthermore, both simulation models show a high average number of persons present in the vaccination center, which goes against the respect of the social distancing condition. Comparison between the two simulation models shows that the proposed model is more efficient than the actual one.

Security-Aware CAN-FD Message Packing in Intelligent Automotive Cyber–Physical Systems

Controller area network with flexible data-rate (CAN-FD) has received great attention in automotive cyber–physical systems (ACPSs) due to its high bandwidth and long payload. However, CAN-FD adopts a broadcast message transmission mechanism and lacks security protection, making it extremely vulnerable to cyberattacks. CAN-FD message packing (packing signals into messages) with low bandwidth occupancy (utilization) under security constraints is the prerequisite for running intelligent applications in ACPS. In this work, we implement a two-stage CAN-FD message packing solution to reduce bandwidth utilization and improve signal acceptance rate under security constraints. The first stage solves the message packing problem of minimizing bus bandwidth utilization under security constraints. The second stage aims at improving the signal acceptance rate by repacking signals. Experimental results show that the first stage reduces average bus bandwidth utilization by 135% compared with the unpacking solution, and the second stage improves the average signal acceptance rate by 5% than existing advanced methods.

Unequal-area capability-based facility layout design problem with a heuristic decomposition-based iterative mathematical programming approach

Thus far in the available literature, capability-based facility layout design approaches were developed by only considering equal machine areas, where they are assigned to the pre-specified locations. Based on this motivation, this paper introduces a new unequal-area capability-based facility layout design (UA-CBFLD) problem for the first time in the literature. In addition to the machines’ unequal-area requirements, the proper distribution of their processing capabilities over the shop floor is considered. First, a new mixed-integer non-linear programming (MINLP) model is developed. Then, a polyhedral inner-approximation method is applied to cope with non-linear area constraints. Due to its NP-hard nature and highly non-linear structure, a heuristic decomposition-based iterative solution approach is also proposed to solve realistic size problems within a reasonable time. In order to show the validity and applicability of the proposed approach, both an illustrative example with different machine-capability overlaps and a real-life application in a manufacturing company are presented. The computational results have shown that the proposed approach has found a 33.25% better layout score (as a function of the distance and the amounts of product flows) and achieved this solution 116.64% faster than the solution of the developed MINLP model via Gurobi’s standard non-linear programming solver. Furthermore, when compared to the existing cellular facility layout design of the manufacturing company, the proposed capability-based unequal-area layout design option has also 29.37% and 34.74% superior layout scores under the scenarios of equal and unequal machine areas, respectively. Moreover, the total average machine utilization has increased up to 89.6%, which is comparatively better than the current average cell utilization, i.e., 65.2%.

Influence mechanism of technological innovation of electric power industry on carbon emission reduction in China

PurposeThis study aims to analyse the scientific relationship between technological innovation and carbon emissions. Taking the Chinese electric power industry as the empirical research object, this study examined the effect of power technological innovation on carbon emissions and proposed policy recommendations for the development of technological innovation in China.Design/methodology/approachThis study first calculated the energy consumption and carbon emission level of the Chinese electric power industry from 2005 to 2018. Secondly, this study built an evaluation index system for technological innovation of electric power with six indicators: average utilisation hours of power generation equipment; power consumption rate of power plant; line loss rate; standard coal consumption for power generation; standard coal consumption for power supply; and number of patent applications granted for generation, conversion or distribution of electric power in China. Finally, from a practical point of view, the relationship between technological innovation and carbon emissions of the Chinese electric power industry from 2005 to 2018 is evaluated and analysed.FindingsPower technology innovation has been found to have a long-term and relatively large effect on carbon emissions, and carbon emissions have a short-term and insignificant impact on power technology innovation.Research limitations/implicationsThis study puts forward relevant suggestions for developing technological innovation and technology transfer, which is essential to establishing a low-carbon or zero-carbon power system in China.Practical implicationsThis study provides empirical evidence for clarifying the relationship between technological innovation and carbon emissions in the power industry and further develops research theories on technological innovation and carbon emissions.Social implicationsRelevant authorities will adopt measures to promote technological innovation and development in the power sector to reduce carbon emissions.Originality/valueThis study built an evaluation index system with six indicators for technological innovation of electric power. The evaluation method was used to measure the technological innovation level of the Chinese electric power industry. The causal link between technological innovation and carbon emissions in China was analysed.

Debris Flow Susceptibility Assessment of Sichuan-Tibet Railway Based on GIS and CF-LR Coupling Model

Sichuan-Tibet Railway is an important transportation system in China, which is of great significance to promote the communication between Tibet and the mainland. This paper takes the lin-chang section of the Sichuan-Tibet railway with a width of 2km on both sides as the research area. The occurrence of debris flow must have appropriate solid source, water source and topographic conditions. In this paper, ten disaster-causing factors are selected, including relief degree of land surface, profile curvature, silty soil, distance to the rive, distance to the fault, elevation, slope aspect, NDVI, average annual rainfall and land utilization rate. The information of disaster-causing factors was extracted from Arcgis and evaluated by the certainty factor model. The probability of debris flow in the study area was calculated by the coupling model of binary Logistic regression model combined with certainty factor model. ROC curve analysis of the CF-LR model shows that AUC is 0.89, indicating that the model has a good effect. The distribution map of debris flow susceptibility in the study area was obtained by using grid calculator in GIS. The results show that under the CF-LR model, extremely low, low, medium and high prone areas cover 2547km₂, 235km₂, 246km₂ and 1099km₂ respectively, accounting for 61.72%, 5.69%, 5.96% and 26.63% of the total area of the study area, respectively. The high risk areas are mainly in Milin, Nyingchi and Bomi counties. This study provides an important reference for assessing the risk of debris flow geological disaster on Sichuan-Tibet Railway.

Multi-objective reinforcement learning framework for dynamic flexible job shop scheduling problem with uncertain events

The economic benefits for manufacturing companies will be influenced by how it handles potential dynamic events and performs multi-objective real-time scheduling for existing dynamic events. Based on these, we propose a new dynamic multi-objective flexible job shop scheduling problem (DMFJSP) to simulate realistic production environment. Six dynamic events are involved in the problem including job insertion, job cancellation, job operation modification, machine addition, machine tool replacement and machine breakdown. As well as three objectives of longest job processing time (makespan), average machine utilization and average job processing delay rate with a set of constraints are also raised in the study. Then this research designs a novel dynamic multi-objective scheduling algorithm based on deep reinforcement learning. The algorithm uses two deep Q-learning networks and a real-time processing framework to process each dynamic event and generate complete scheduling scheme. In addition, an improved local search algorithm is adopted to further optimize the scheduling results and the idea of combination is used to make the scheduling rules more comprehensive. Experiments on 27 instances show the superiority and stability of our approach compared to each proposed combined rule, well-known scheduling rules and standard deep Q-learning based algorithms. Compared to the current optimal deep Q-learning method, the maximum performance improvement for our three objectives are approximately 57%, 164% and 28%.

A Test for Complementarity or Substitutability between Foreign Direct Investment (FDI) and International Trade: Evidence from Nigeria’s Non-Oil Sector

This study investigated the dynamic relationship between foreign direct investment and trade in Nigeria’s non-oil sector between 1981 and 2015. Though the theoretical literature argues that there could be complementarity or substitutability between them, the empirical literature has remained inconclusive. Invoking the product life-cycle theory proposed by Vernon (1966) and employing the Autoregressive Distributed Lag (ARDL) framework, we examined the nature of the relationship between non-oil trade (both imports and exports) and inward non-oil FDI stock while controlling for real gross domestic product, real effective exchange rate, and average manufacturing capacity utilization. Our empirical results showed a significant complementary relationship between Nigeria's inward non-oil FDI stock and non-oil exports. Meanwhile, no relationship was observed between inward non-oil FDI stock, non-oil import, and non-oil total trade. To this end, inward FDI stock in Nigeria’s non-oil sector complements the sector’s exports, automatically boosting non-oil exports. We also established that economic growth reinforced the complementary relationship between inward FDI stocks in the non-oil sector and aggregated and disaggregated trade measures, particularly over the short-term horizon. Therefore, appropriate inward FDI stock is required to boost the export performance of Nigeria’s non-oil sector, which would help diversify the fiscal revenue and foreign exchange sources away from the traditional oil and gas sector. Keywords : Inward FDI stock, non-oil trade, Complementarity, Substitutability, Nigeria, JEL Classification: C51, F12, F23. DOI: 10.7176/JESD/13-22-03 Publication date: November 30 th 2022

Health service use before and after dementia diagnosis: a retrospective matched case–control study

ObjectivesThis study investigated patterns in health service usage among older adults with dementia and matched controls over a 10-year span from 5 years before until 5 years after diagnosis.DesignPopulation-based retrospective...

PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks

5G/B5G is intended to support various services with the assistance of network slices, and each slice requires adequate resources to provide the prenegotiated service quality. The Slice Admission Control (SAC) algorithm is a necessity for Slice Providers (SPs) to guarantee the Quality of Service (QoS) and Quality of Experience (QoE) of each admitted request. In such circumstances, concerns regarding the priority of services and the fairness of resource allocation are meaningful topics. However, these two issues do not receive sufficient research attention simultaneously in the literature. Here, we study the SAC problem in 5G/B5G networks, aiming to enhance the fairness degree while satisfying the necessary priority requirements. We first reinterpret priority as a higher Cumulative Service Acceptance Ratio (CSAR) and adopt the uniformity of adjacent CSAR gaps to reflect fairness. The SAC problem is formulated as a nonlinear and nonconvex multiobjective optimization problem. Thus, we propose a heuristic algorithm called Prioritized Slice Admission Control Considering Fairness (PSACCF) to solve it. This approach introduces the resource efficiency of services to amend priority violations and then promotes fairness by setting the target CSARs for each service type and improving their actual CSARs. Numerous simulations are conducted to compare the performance of PSACCF with that of Moderate High Priority First (MHPF) and Aggressive High Priority First (AHPF). The results show that PSACCF can achieve at least a 33.6% improvement in fairness degree and a higher minimum average resource utilization while maintaining a nearly identical priority indicator to that of the compared methods.

단독주택 리모델링 시 환기요소 적용에 따른 에너지소비량 및 쾌적성 비교

As demand for low-energy buildings increases, indoor environment, and ventilation evaluation is becoming critical. However, it is difficult to find studies that evaluated complex factors other than mechanical ventilation, such as void, natural ventilation, IoT, and the number of occupants. Therefore, in this study, a total of seven scenarios were established based on four ventilation factors, and general buildings before remodelings and low-energy building after remodeling were analyzed based on three evaluation indicators (energy usage, discomfort time, and ACH). In conclusion, energy consumption was most affected by ventilation methods, and building performance also had ample influence that low-energy buildings had an average utilization rate of 30% compared to general buildings. Discomfort time was most affected by the ventilation method, which had RreliefF contribution was 42%, and natural ventilation was likely to be exposed to high discomfort time regardless of other ventilation factors. Finally, in the case of ACH, low-energy buildings did not meet the average minimum standard in all scenarios when only indoor temperature and humidity were considered as ventilation standards. Rather, ventilation in general buildings meets the criteria for ACH values, so it is necessary to consider whether ventilation can be performed simultaneously with temperature, humidity, and comfort.

Performance analysis and modelling of a 50 MW grid-connected photovoltaic plant in Spain after 12 years of operation

This study aims to estimate the performance and losses of a 50 MW photovoltaic (PV) utility-scale after 12 years of operation. The PV plant has monocrystalline and polycrystalline silicon modules and is located in the central region of Spain with an annual insolation of 1976 kWh/m2. Monitoring data over the entire year 2020 has been analyzed and filtered to assess the performance results following the IEC 61724 standard guidelines. The annual average reference yield, final yield, performance ratio and capacity utilization factor are of 5.44 h/d, 4.28 h/d, 79.24%, and 19.77%, respectively. Besides the experimental analysis, this work improves the estimation of the daily performance ratio, especially in days with low insolation. Two different modelling approaches have been assessed and compared. In first place, a physical model has been adopted, based on the most common losses, and including an exponential expression to account for low irradiance losses. In second place, statistical models have been used, with either multiple linear regressions or random forest algorithms. In contrast with other published models which require many inputs, the best accuracy has been reached with the random forest model using only the ambient temperature and solar irradiance as predictors, obtaining a RMSE of 1% for the PR and for the energy production.