Year-round Performance Research Articles

Year-round performance evaluation of a solar-powered compact HDH desalination system for remote water scarce regions

ABSTRACT Climate change is intensifying global hydrological stress, increasing evaporation, degrading water quality, raising water temperatures, and altering runoff periods. This study introduces a fully solar-powered humidification-dehumidification (HDH) desalination system designed to address water scarcity in remote and arid regions. The significance of the research lies in its potential to provide a sustainable, off-grid water supply using renewable solar energy, which is particularly relevant in areas with limited infrastructure. The aim of the study is to evaluate the year-round performance of the system by developing a comprehensive mathematical model based on mass, energy, and momentum conservation equations. The model simulates the system’s behavior under varying solar conditions, enabling the optimization of key parameters. The system combines thermal photovoltaic panels and evacuated tube solar water collectors, enhancing energy efficiency through the pre-heating of seawater. The model is implemented in MATLAB, and the performance is evaluated over a full annual cycle. Results show that the system achieves a peak efficiency of 42% during high solar months, with monthly productivities of up to 238.87 L/m2, while the lowest performance occurs in December, with 14% efficiency and 63.02 L/m2 productivity. These findings underscore the system’s capability to provide a sustainable water source year-round.

Evaluating inverse modeling methods for measurement and verification of chiller energy efficiency measures

A commonly budget and time constrained yet crucial measurement and verification process for chiller energy efficiency measures often requires rarely trended chiller performance data for the pre-retrofit or post-retrofit chiller necessitating chiller modeling for year-round performance prediction. This study evaluates chiller inverse modeling methods for measurement and verification applications. Variable speed drive-controlled centrifugal chillers operating in a hot and humid climate are used as case studies. Two scenarios are explored: one where full-range metered chiller data are available and another with limited data that requires a short-term metering process. The biquadratic black-box and Gordon-Ng with a variable entropy term models perform exceptionally well when full-range chiller performance data is available, displaying a coefficient of variation of approximately 5 % for both training and test datasets. However, in situations that use short-term metered data not covering the full range, the fundamental and Foliaco reformulated Gordon-Ng models outperform other models. These models show minimal degradation in average statistical performance when trained with one-month metering data instead of four-month data, indicating their potential to capture the year-round chiller performance variation with short-term metered data. Furthermore, the optimal metering period for an accurate modeling process is identified as one containing data from at least one shoulder month like April, May, or October for a hot and humid climate. These findings provide valuable guidance for practitioners involved in chiller efficiency measure assessments, emphasizing the significance of proper model selection and an appropriate metering period for a reliable measurement and verification process.

Integrating dual heat sources to enhance thermoelectric generator power output

The escalating demand for sustainable power sources for wearable electronics necessitates innovative solutions. This study tackles the challenge of sustainably powering wearable electronics, addressing the limitations of traditional batteries. The approach utilizes a thermoelectric generator with a flexible and stretchable substrate that integrates the photothermal effect and human body temperature as a heat source. A unique strategy is employed to achieve this, involving the combination of 10 g of polydimethylsiloxane, 0.5 g of carbon nanotubes powder, 1 g of base curing agent, and 1.15 g of ethyl acetate mixture used as the novel substrate material. The proposed composite substrate is paired with p-type and n-type thermoelectric legs of bismuth antimony telluride (Bi0.4Sb1.6Te3) and bismuth selenium telluride (Bi1.7Te3.7Se0.3), respectively. This innovative design ensures high flexibility, stretchability, and conformability, facilitating seamless integration into wearable electronic devices. Experimental validation and simulation-based studies reveal a power density of 166.29 μW/cm2, sufficient for powering small-scale wearable electronics. The thermoelectric generator coupled with a novel composite substrate showed 65.6 % stretchability, further underscoring its durability and adaptability. The critical finding lies in the dual heat source integration, which collectively boosts power output and ensures year-round performance, pushing the boundaries of wearable energy harvesting technologies.

Year-round performance evaluation of photovoltaic-thermal collector with nano-modified phase-change material for building application in an arid desert climate zone

This study provides a comprehensive analysis of the energetic and thermoelectric characteristics of a solar photovoltaic-thermal (PVT) collector equipped with a nano-modified phase-changing material (NPCM) for PV cooling. The operational effectiveness of the collector is evaluated using various parameters such as PV temperature, electrical and thermal powers, melting time, PCM liquid fraction, and PV cooling duration. These factors are meticulously examined under different operating conditions of solar irradiance, ambient air temperature, and the thickness of the NPCM layer. Hail, Saudi Arabia, a city known for its arid desert climate, is chosen as the geographic setting for the study. A numerical methodology based on finite volume discretization is used to solve the governing mathematical models. The year-round performance of the combined PVT and NPCM unit is systematically assessed in terms of monthly averages across the four seasons, considering varying NPCM layer thickness over different time frames. The results indicate that an NPCM with a nanoparticle volumetric concentration of 5% and an appropriately chosen NPCM layer thickness provides effective PV cooling, with cooling durations ranging from 54 to 305 min throughout the year. In terms of electrical performance, months with high solar irradiance yielded a greater power output, peaking at approximately 52.5 Watts, while the winter months exhibited the lowest output. The thermal performance followed a similar pattern, demonstrating the significant influence of solar irradiance on thermal efficiency, with peak outputs reaching up to 225 Watts during high solar intensity months.

Application of ANN control algorithm for optimizing performance of a hybrid ORC power plant

Hybrid systems for generating electricity from multiple sources are becoming an increasingly popular subject of analysis in science and industry. This paper presents a validated model of a hybrid ORC plant powered by solar and geothermal energy. A key challenge in optimizing the operating parameters over time was the variability of solar conditions, which was the main energy source of the system. The operation of the ORC plant is simulated using a complex model with Multiple Input Multiple Output (MIMO) variables, which is nonlinear. The input variables represent the system’s operational parameters, while the output variables describe the plant’s performance indicators. The main objective of this paper is to optimize the year-round performance of the ORC installation through different computational techniques. The first approach involves the application of the gradient-based optimization method that is known as sequential quadratic programming (SQP). With the use of SQP, two distinct simulation runs (SQP-N and SQP-Q/N) of the system are performed, each with a specific objective function to be optimized. The second approach is based on reinforcement learning principles and leverages the method known as Deep Deterministic Policy Gradient (DDPG) algorithm. The main advantage of DDPG over SQP is that DDPG does not require knowledge of the model. This improves the algorithm flexibility, making it well-adapted to fluctuating environmental conditions. Overall, three optimization runs (two using SQP, one using DDPG) have been performed, aiming at identifying the optimal year-round control strategy for the installation. The results revealed that under the control of DDPG, the hybrid system has produced the highest amount of electricity (4993.4 MWh), outperforming in this matter SQP-N and SQP-Q/N optimization variants by 16.83 % and 10.49%, respectively.

Microalgae bio-reactive façade: A radiative-convective model powered by hourly illumination computation and historical weather data

This article presents a numerical model predicting the performance of a microalgae biofaçade. The core of the model is the association of radiative, convective, and conductive heat transfers. These key physical phenomena are modulated by actual weather data and coupled to a biological model of the microalga Chlorella vulgaris. Based on the conjunction of temperature and illumination predictions, the biofaçade performances are categorized between low, adequate, high temperature × light-deficient, light-sufficient conditions. The capabilities of the model (dissection of acute events to year-round performance prediction) are illustrated using the city of Marseille, France, over the year 2020. The numerical behavior of the model itself is then analyzed. The influence of the Urban Heat Island submodel is discussed, and a global sensitivity analysis (Sobol’s indices) is led to assess the impact of uncertain physical parameters. The model accuracy is evaluated at 7.4% of the total daytime (under conservative assumptions) . The most influential parameters are the microalgae culture emissivity and the building indoor emissivity. All in all, the confidence in this model is high enough so that it can be used to design a biofaçade numerically.

Dynamic performance forecast analysis of MHP-PV/T-IDXSAHP system during heating season

MHP-PV/T is a novel PV/T with phase change heat transfer, uniform flow of working medium and good anti-freeze performance. The excellent year-round performance of MHP-PV/T has been demonstrated in previous studies and is more suitable for winter heating in northern China. In this work, we further combine MHP-PV/T with dual source heat pump to form an experimentally validated indirect expansion-solar assisted heat pump (IDX-SAHP) system and simulates the overall performance of the heating system in winter based on TRNSYS in Lanzhou, China. The component type50b of TRNSYS was modified to make it closer to the actual results of MHP-PV/T. Research shown that the thermal efficiency of MHP-PV/T heat pump system can be maintained well throughout the heating season at the SAHP/ASHP switching temperature is 7 ℃ in winter. The net system energy efficiency ratio (NSEER) during heating season is range from 3.28 to 5.42, which can achieve highly efficient and low cost heating. For this system, the photovoltaic power generation is sufficient to meet the operation of three water pumps. By connecting surplus electricity to the grid, a total of 1806.25 ¥ of electricity revenue can be obtained around the year. The PEUR of the system is 72.26 %, the DPP taking 5.133 years, and the annual CO2 emission reduction is 13486.5 kg. Significant energy, economic, and environmental benefits of the system. This study demonstrates the application prospects of PV/T heat pumps and provides a reference basis for subsequent research on MHP-PV/T heat pump systems in cold regions.

Parametric investigation and optimization of phase change material-based thermal energy storage integrated desiccant coated energy exchanger through physics informed neural networks oriented deep learning approach

To improve the energy exchange abilities, and to enhance indoor air quality, a desiccant-coated energy exchanger (DCEE) is a capable substitute compared to conventional energy exchangers such as fixed beds and desiccant wheels. Thus, accurately predicting the DCEE's physical characteristics is crucial for enhancing the system's performance. Therefore, in the current study, a novel data-driven modeling methodology utilizing physics-informed neural networks (PINNs) is developed to predict the exit parameters of DCEE, like the outlet temperature of cooling water, the outlet temperature of the air, and the outlet-specific humidity of the air. The performance characteristics of DCEE are evaluated using PINN by considering different input parameters. The performance parameters selected for the DCEE performance assessment are dehumidification capacity, thermal coefficient of performance, and heat recovery efficiency. The coated desiccant material chosen for the present study is silica gel. Good agreement is obtained between the PINN and experimental results for both the steady-state and transient cases, proving the PINN method's capability in solving multiple physics-based partial differential equations (PDEs) on a single domain with a maximum discrepancy of ±7.8 %. The developed model is used to obtain the optimal inlet conditions for a given operating condition. Adsorption kinetics of the coated desiccant silica gel revealed that the maximum water uptake capability for the given operating range is 0.345 g.g−1. A case study has been carried out by integrating an industrial waste heat-driven DCEE with a thermal energy storage module (TES) to analyze the energy storage ability during regeneration. A parametric study has been conducted to examine the transfer characteristics of the TES module. Further, the effectiveness of the TES module for five different phase change materials (PCMs) has been assessed. Finally, the seasonal energy efficacy ratio (SEER) of five different PCMs was investigated for 365 days to evaluate year-round performance. The effectiveness of thermal energy storage is maximum for Palmitic acid and minimum for Climsel C48, with corresponding values of 0.73 and 0.37. SEER is the largest for Palmitic acid and lowest for Climsel C48. The values of SEER for Palmitic acid during the summer and winter seasons are 5.489 and 3.31, whereas the values of SEER for Climsel C48 during the summer and winter seasons are 5 and 2.89, respectively.

Experimental study and model development of bifacial photovoltaic power plants for Indian climatic zones

Bifacial photovoltaic technology is considered an emerging and promising PV technology. However, the unavailability of proven field data or efficient models prevents the stakeholders from going ahead with this technology. The currently available bifacial performance models have limitations; they are location specific, for a single bifacial PV module alone, or ground-level installed bifacial PV only. This paper develops an experimentally validated view factor-based optical model using the Cross-string rule to predict the power output and bifacial gain for bifacial PV power plants with good accuracy and low computation power and time. An optimal model for the best performance of bifacial PV at any location was also suggested by optimizing the parameters like the pitch, height of installations, and slope. The results show that the year-round performance of bifacial PV in cities in India's five climatic zones is 30.54%–34.93% higher than the monofiacial PV panels at an average ground albedo of 30%. Bi-annual tracking improved a bifacial PV's overall power output and bifacial gain. The financial indicators like Levelised cost of energy, Net present value, Discounted payback period, and Internal rate of return indicate higher financial gain from the bifacial PV power plant compared to monofacial PV power plant.

Tilt Angle of Solar Panels for Best Winter, Summer and Year-Round Performances for Different Regions of the World

Background:
 In recent years, solar energy has gained increased attention. The energy from the sun is unlimited and environment friendly. It is useful in decreasing the carbon dioxide emission that comes from the burning of fossil fuels and leads to global warming.
 Materials and Methods:
 The tilt angle of a solar panel is an important parameter that affects its performance. This paper provides the tilt angle of solar panels for 90 capital cities in 90 countries in the northern and southern hemispheres. Solar Irradiance Calculator is used to calculate the tilt angles from vertical.
 Results:
 The tilt angle for the studied capital cities ranges from 11° to 90° in winter, 41° to 105° in summer and 26° to 90° for year-round. The output results obtained from the calculator are very close to those calculated from equations and agree with previous studies.
 Conclusions:
 According to the results of the current work, only the latitude is required to calculate the tilt angle at any location worldwide. These results can be generalized to any location on the earth. The results of this work are expected to give valuable information to the users of solar panels.

Design procedure of stationary compound parabolic concentrator with flat plate absorber for effective year-round performance – Response Surface Methodology and Tracepro as tools

The stationary Compound Parabolic Concentrators (CPC) with a low concentration ratio (C<2) are suitable for pre-heating processes in industrial applications. The reported literature does not analyze the radiation available or the solar angles for whole year towards design of CPC which could provide maximum annual energy. This paper optimizes the design factors of CPC, such as acceptance half-angle (θa) and truncation ratio (TR), to yield maximum energy around the year, maximize optical efficiency and minimize reflector material. Annual irradiation data for the location - Tiruchirappalli, Tamilnadu, India (ϕ- 10°45′36″ N) is considered for the present study. The raw irradiation data is processed to get monthly average daily data, which is used as input for the TracePro v21.1. The experimental design (DOE) is made in response surface methodology (RSM) by considering the factors of θa - 18.5 to 28.5° and TR - 0.5 to 1.0. Nine Geometric models were created using Solidworks based on the combination of parameters given by RSM and simulated using TracePro to estimate the annual energy collection and average optical efficiency. Using the TracePro analysis month wise energy collection is calculated and reported. Further, a model relating θa and TR to the annual energy collected was obtained. According to the results obtained from the multi-objective optimization, the optimum concentration ratio is 2 with the θa of 28.5° without truncation. The results also indicate that the deviation from the average energy collected by the optimized design over the year is less when compared to the other designs.

Annual Thermal Management of the Photovoltaic Module to Enhance Electrical Power and Efficiency Using Heat Batteries

Several studies state that phase change material (PCM) improves the electrical power and efficiency of the photovoltaic (PV) module. To find the suitable PCM for tropical climatic conditions, multi-PCMs are examined simultaneously with melting temperatures of 31 °C, 35 °C, 37 °C, and 42 °C. In this study, PCM containers are integrated behind the PV module with a thickness of 50 mm. The performance of the multi PV-PCMs is monitored year-round and compared with PV-noPCM. The experimental results show that the selected four PCMs performed the cooling process autonomously in all the climates, such as PCM with a melting temperature of 37 °C and 42 °C enhanced the higher cooling rate in summer, and the same PCMs failed to achieve a higher cooling rate in winter. The lowest temperature drop was noted for pre-monsoon and monsoon seasons due to the low irradiance. On the other hand, the highest temperature drop of 16.33 °C is observed for pre-summer (March) and 15.7 °C, and 17.14 °C for summer (April) as compared to PV-noPCM. The results of the present investigation highlight the requirement for choosing the proper PCM melting temperature based on optimal year-round performance. Further, it is recommended that a single PCM melting temperature for cooling the PV modules year-round in tropical climates is inappropriate, and instead, a cascaded structure with different PCM melting temperatures is recommended.

Highly Efficient Year-Round Energy and Comfort Optimization of HVAC Systems in Electric City Buses

In this paper, we present a novel approach to perform highly efficient numerical simulations of the heating, ventilation, and air-conditioning (HVAC) system of an electric city bus. The models for this simulation are based on the assumption of a steady-state operation. We show two approaches to obtain the minimum energy requirement for a certain thermal comfort criterion under specific ambient conditions. Due to the computationally efficient approach developed, we can evaluate the model on a large dataset of 7500 scenarios in various ambient conditions to estimate the year-round performance of the system subject to different comfort requirements. Compared to a heating strategy based on positive temperature coefficient (PTC) elements, we can thus show that a heat pump (HP) can reduce the annual mean power consumption by up to 60%. Ceiling-mounted radiant heating elements complementing a PTC heating system can reduce the annual mean power consumption by up to 10%, while they cannot improve the energy efficiency when used in conjunction with a HP. Finally, a broad sensitivity study reveals the fact that improving the HP's heating-mode coefficient of performance (COP) manifests the largest leverage in terms of mean annual power consumption. Moreover, the annual energy expenditure for cooling are around eight times smaller than those for heating. The case study considered thus reveals that the advantages of improving the COP of the cooling mode are significantly lower.

Long-term performance evaluation of CO2 heat pump water heater under different discharge pressure control strategies

CO2 heat pump water heater has raised considerable attention as an environmentally efficient technology for hot water production. However, after long-term operation, the deterioration of component performance will not only directly cause system performance degradation, but also lead to a deviation of discharge pressure from the actual optimum under different discharge pressure control strategies, further resulting in additional performance loss. So far, the additional performance loss in the long run has not been in-depth investigated. In this study, based on a validated system model of CO2 heat pump water heater with component performance degradation factors, the long-term additional loss of system performance under eight different discharge pressure control strategies are compared. The results reveal that additional performance loss in the long-term operation can be significant under certain control strategy and working conditions. As for the investigated system, the control strategy with the parameter group of ambient and gas cooler outlet temperatures is favorable for application under the nominal operating condition with long-term degradation of heat exchangers, and the average additional loss of COP (coefficient of performance) can be decreased to 0.62%. As for the compressor degradation, the optimal refrigerant charge management strategy is preferred with an average COP loss as low as 0.05% under certain working conditions. In facing with year-round performance degradation, the control strategy with the parameter group of evaporating temperature and gas cooler outlet temperature shows better robustness, since the average additional annual performance loss can be reduced to only 2.52%.

Design of a Compact Dry Cooler With an Aluminum Heat Exchanger Core for a Supercritical CO2 Power Cycle Is Evaluated for a Concentrating Solar Power Application

Abstract As the supercritical CO2 power cycle develops and the component technologies mature, there is still a need to reduce the associated capital and operating costs to maintain a competitive levelized cost of electricity (LCOE) in order to enter the market. When considering concentrating solar power (CSP) coupled with an sCO2 power block and sensible thermal storage, the technology presents a clean source for utility-scale power generation to support baseload or peak-load electrical demand. However, the LCOE of the technology is still considered higher than the competing technologies and should be reduced to better compete in the market; 2030 targets for dispatchable solar plants are 5¢/kWh for baseload CSP and 10¢/kWh for peaker plants, as set by the United States Department of Energy. In response to this need, this study is targeting improvements in the power cycle precooler to reduce power block contribution to LCOE. This study considers a dry cooler, as CSP plants are sensitive to water consumption because many installations are slated for remote or arid locations where solar irradiance is very high, but water is scarce. Furthermore, the power block footprint for an sCO2 system is quite compact, especially as compared to a steam cycle. Therefore, there is interest in installing a more compact dry cooler that is proportional to the reduced footprint sCO2 power block, while conventional dry coolers are an order of magnitude larger. The competing goals of size, performance, and cost were considered in this study to develop a compact dry cooler that can easily be packaged with the power block, significantly reducing the installation and transport cost compared to the current state of the art, while maintaining or improving upon the heat transfer performance and impact on plant LCOE. This paper details the high-level findings of a large dry cooler sensitivity study for design point selection, design of the compact dry cooler, expected year-round performance for the dry cooler and the power cycle, and the predicted LCOE for a 30-year plant life. It was found that an aluminum heat exchanger core can be suitably designed to meet the pressure and temperature requirements for a precooler in an sCO2 recompression Brayton cycle. The dry cooler assembly was found to have improved heat transfer performance, allowing for increased cycle efficiencies and a reduced plant LCOE. When coupled with a centrifugal blower and compact transition duct, the dry cooler assembly was able to reduce the installation footprint by over 50%.

Performance Evaluation and Estimation of Energy Measures of Grid-Connected PV Module

In this paper, the effectiveness of two grid-connected photovoltaic (PV) techniques up of copper indium selenium (CIS) and monocrystalline silicon (m-Si) arrays has been examined. In order to determine whether the technology is suitable for the actual winter and summer climatic conditions in Thoothukudi, Tamil Nadu, the observed and calculated performances have been compared. The final yield, photovoltaic (PV) effectiveness, array yield, performance ratio, and capacity utilisation factor seem to be the variables used to evaluate performance. Using recorded meteorological data at the selected location, PVsyst software predicts both PV systems’ year-round performances. These predictions are then contrasted to the outcomes of the actual measurements. The outcome showed that with a maximal observed performance ratio, both PV systems function marginally better in the winters than those in the summers. The performance indicators of the PV mechanisms are contrary with those of other PV systems with comparable capacities that are located in different places.

The technical, economic, and environmental feasibility of a bioheat-driven adsorption cooling system for food cold storing: A case study of Rwanda

This paper studies the technical, economic, and environmental feasibility of a standalone adsorption cooling system thermally driven by biomass combustion and solar thermal energy. The developed cooling package was benchmarked against a baseline vapour compression refrigeration system, driven by grid electricity and the widely investigated adsorption cooling system driven by solar heat. TRNSYS was utilised to imitate the integrated systems, investigate their year-round performance, and optimise their designs by employing the meteorological data for Rwanda and an existing cold room (13 m2 floor area × 2.9 m height) as a case study. The optimisation study for the system revealed that maximum chiller performance (COP = 0.62), minimum biomass daily consumption (36 kg), and desired cold room setting temperature (10 °C) throughout the year can be achieved if the boiler setting temperature, heat storage size, and heating water flow rate are 95.13 °C, 0.01 m3 and 601.25 kg/h. An optimal PV area/battery size combination of 12 modules/16 kWh was obtained from the economic, environmental, and technical viewpoints.

Performance of a mechanically-driven loop heat pipe heat recovery system

A mechanically-driven loop heat pipe heat recovery system by booster and refrigerant pump was proposed to match the all-year fresh air load varying greatly with ambient temperature in an energy recovery ventilation unit and enhance its energy-saving potentials. The system prototype was developed and the experimental setup established in which the booster and pump can operate together or separately. Namely, the prototype could be running in pump-driven loop heat pipe (PLHP) mode, booster-driven loop heat pipe (BLHP) mode or booster combining with pump-driven loop heat pipe (CLHP) mode. The heat transfer characteristics of the prototype running in these three modes under winter and summer conditions were studied, respectively. The results showed that the temperature effectiveness of CLHP was greater than that of PLHP or BLHP under all-year conditions. When outdoor temperature is −15 °C, the temperature effectiveness of CLHP is 78.0% and 52.5% higher than that of PLHP and BLHP, respectively, and the heating EER of CLHP is 29.6% higher than that of BLHP. When outdoor temperature is 40 °C, the CLHP has 19.5% higher of temperature effectiveness and 21.7% higher of cooling EER comparing with the BLHP, respectively. In winter, BLHP performs better when outdoor temperature is greater than 7.5 °C while CLHP performs better when outdoor temperature is lower than 7.5 °C. And BLHP has better performance when outdoor temperature is lower than 35 °C in summer while CLHP performs better when outdoor temperature is higher than 35 °C. The composite system can switch its operating mode according to the fresh air load, which can improve effectively the year-round performance of the system to recover heat in building ventilation.

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.

Using statistical tests to compare the coefficient of performance of air source heat pump water heaters

The study compared the coefficient of performance (COP) of two residential types of air source heat pump (ASHP) water heaters using statistical tests. The COPs were determined from the controlled volume of hot water (150, 50 and 100 L) drawn off from each tank at different time of use (morning, afternoon and evening) periods during summer and winter. Power meters, flow meters, and temperature sensors were installed on both types of ASHP water heater to measure the data needed to determine the COPs. The results showed that the mean COPs of the split and integrated type ASHP water heaters were 2.965 and 2.652 for summer and 2.657 and 2.202 for winter. In addition, the p-values of the groups COPs for the split and integrated type ASHP water heaters during winter and summer were 7.09 x 10-24 and 1.01 x 10-11, based on the one-way ANOVA and the Kruskal-Wallis tests. It can be concluded that, despite the year-round performance of both the split and integrated type ASHP water heaters, there is a significant difference in COP at 1% significance level among the four groups. Furthermore, both statistical tests confirmed these outcomes in the comparisons of the mean COPs among the four groups based on the multiple comparison algorithm.