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Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

Incorporating distributed renewable energy sources into heating, power, and cooling systems is facilitating the drive toward intelligent building energy solutions. However, the inherent uncertainties associated with controllable renewable resources pose challenges for the thermo-economic scheduling of smart buildings. Nonetheless, the flexibility inherent in smart buildings can be leveraged through various market mechanisms. Hence, this research introduces a sustainable energy-building system driven by an autonomous solar/dish Stirling engine (SDSE) and wind turbine combined with a sophisticated control strategy and battery storage, which is designed to provide heat and power requirements. The proposed control strategy is also devised to optimize energy generation, ensuring prudent conservation and minimal waste, thereby amplifying overall efficiency and financial viability. The meticulous design of the system is accomplished through advanced MATLAB/Simulink® modeling. A thorough technical sensitivity analysis meticulously hones design parameters, revealing optimal operational thresholds. Simulation outcomes unveil a consistent Stirling engine (SE) efficiency, achieving a pinnacle of 35 %, while the SDSE attains over 28 %, respectively. The horizontal axis wind turbine, encompassing 100 kW and 500 kW modules, demonstrates power coefficients spanning from 0.18 to 0.09, with corresponding land area requirements ranging from 4.22 m2 to 21.10 m2, emphasizing the pivotal roles of module power and land area optimization. This paper also casts a spotlight on the environmental repercussions of the system, illustrating its potential to avert up to 30,000 kg of CO2 emissions per kWh/year. Moreover, the levelized cost of electricity ranges from 0.13 to 0.16 $/kWh, accompanied by an hourly cost that fluctuates between 3 $/h and 40 $/h, respectively. Conclusively, the developed modeling can be regarded as a significant stride in the realm of hybrid renewable energy systems, replacing the conventional photovoltaic/wind models with cutting-edge solar/wind configurations managed by a sophisticated control strategy and battery storage system.

This article outlines the input data and partial shading conditions employed in the replication model of Sequential Monte Carlo (SMC)-based tracking techniques for photovoltaic (PV) systems. The model aims to compare the performance of classical perturb and observe (P&O) algorithm, particle swarm optimization (PSO) algorithm, flower pollination algorithm (FPA), and SMC-based tracking techniques. The mathematical design and methodology of the complete PV system were detailed in our prior research, titled ''Dynamic and Adaptive Maximum Power Point Tracking Using Sequential Monte Carlo Algorithm for Photovoltaic System'' by Odat et al. (2023) [1]. The provided data facilitate precise replication of the output, saving significant simulation time. Additionally, these data can be readily applied to compare algorithmic results referenced by (Babu, T.S. et al., 2015; PrasanthRam, J. et al., 2017) [2,3], and contribute to the development of new processes for practical applications.

The hike in fuel prices and rapid depletion of fuel reserves have compelled scientists to focus on energy conservation, environmental protection, engine performance improvement, and cost saving. The prime objective of the study is to compare the empirical results with response surface methodology (RSM) optimized results in order to check the accuracy of model designed by RSM. Therefore, the current study examines the effect of fuel injection parameters (nozzle opening pressure and protrusion) on diesel engine performance and exhaust emissions. RSM technique was applied to predict engine performance and exhaust emission parameters along with their optimization. The brake thermal efficiency (BTE) was incremented by 1.23 % for protrusion from 1.5 to 2.5 mm under 240 bar nozzle opening pressure (NOP). BTE was increased by 0.94 and 4.51 % for 1.5 and 2.5 mm protrusion respectively. CO emission was decremented by 4.47 and 11.31 % for 1.5 and 2.5 mm protrusion respectively when the NOP changed from 230 to 240 bar. RSM model optimized input conditions 240 bar pressure, 2.5 mm protrusion, and 1935.67 engine rpm. The engine was again tested on RSM-optimized conditions and the highest absolute percentage error (APE) of 4.42 % was obtained for NOx emission, while the lowest APE of 2.89 % was obtained for BSFC.

The primary purpose of this research is to determine the most economical approach to installing a solar dish Stirling engine (SDSE) system on a building for residential purposes in Mafraq while taking into account the local weather, usual monthly consumption of energy and the prices charged by the local powered utility. The house uses an average of 622.25 kWh of energy every month, with the highest consumption in February and the lowest in May. A range of optical efficiencies between 50% and 98% are used to mount the SDSE system. This study evaluated the relationship between the price of electrical energy and the amount of power consumed to identify the times of day when energy consumption is highest. Another approach relevant to consider is solar power, which likewise varies across the whole year. When the available intensity of the sun and power rates are at their peak, an SDSE system is regarded as a feasible solution for fulfilling the energy requirements. This is because SDSE systems can still make electricity even during cloudy days. This work also includes a comprehensive analysis of the solar power that an SDSE receives and the generated electrical power. [ABSTRACT FROM AUTHOR]

To produce maximum output power in a solar collector system, a maximum power point tracker (MPPT) is considered a vital component in system design. Though, due to partial shading effect initiated by dynamic weather conditions, the tracking process becomes more complex when locating the global maximum power point (GMPP). To solve this issue, an innovative and adaptive MPPT based on sequential Monte Carlo is suggested to accurately and efficiently predict the global peak under rapid changing weather conditions. The proposed method adaptively predicts the next best duty cycle value that will generate the maximum output power. The capability of the technique has been tested strongly under standard test conditions (STC) and dynamic weather conditions including random partial shading and changing irradiance and temperature input values. The new recommended technique is compared to the classical perturb and observe algorithm, in addition to the particle swarm optimization, and flower pollination tracking techniques. The simulated results illustrated dominance in accuracy and efficiency under varying environment conditions. The efficiency calculated was found to be as high as 99.89% at STC and as low as 98.58% at dynamic and random partial shading conditions. In addition, the results displayed high tracking speed in predicting the GMPP while maintaining no oscillations at the output power. [ABSTRACT FROM AUTHOR]

Excessive wear of gears will not only cause noise and vibration in the transmission system, but also reduce transmission efficiency and accuracy in severe cases, causing irreversible losses to the transmission system. It is desirable to develop a micro-gear unit model for evaluating the wear process and predicting the failure time of large gear units (such as wind turbine gear units), reducing losses due to sudden failures. Based on the Buckingham pi-theorem of dimensional analysis and Hertz formula, the similarity ratio of each parameter of the gear wear process was proposed. The maximum equivalent stress is calculated by establishing the FEM model and comparing it with the theoretical contact stress calculated by the Hertz formula, and the results were relatively consistent. Two pairs of gear friction and wear experiments with similar parameters were carried out to compare the wear evolution performance of two similar gears. The friction performance process of the test gears was observed by particle counter and analytical ferrograph. The results show that the friction and wear processes of the two groups of gears with similar parameters have a certain correlation, which was consistent with the proposed similarity model. The similarity model combined with the observation results of abrasive particles has a certain application value for the evaluation of the wear state of the transmission system. [ABSTRACT FROM AUTHOR]

The problem of spam e-mail has gained a tremendous amount of attention. Although entities tend to use e-mail spam filter applications to filter out received spam e-mails, marketing companies still tend to send unsolicited emails in bulk and users still receive a reasonable amount of spam e-mail despite those filtering applications. This work proposes a new method for classifying emails into spam and non-spam. First, several e-mail content features are extracted and then those features are used for classifying each e-mail individually. The classification results of three different classifiers (i.e. Decision Trees, Random Forests and k-Nearest Neighbor) are combined in various voting schemes (i.e. majority vote, average probability, product of probabilities, minimum probability and maximum probability) for making the final decision. To validate our method, two different spam e-mail collections were used. [ABSTRACT FROM AUTHOR]

Relevance feedback methods generally suffer from topic drift caused by word ambiguities and synonymous uses of words. Topic drift is an important issue in patent information retrieval as people tend to use different expressions describing similar concepts causing low precision and recall at the same time. Furthermore, failing to retrieve relevant patents to an application during the examination process may cause legal problems caused by granting an existing invention. A possible cause of topic drift is utilizing a relevance feedback-based search method. As a way to alleviate the inherent problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with phrases disambiguating the original query words. The idea was implemented for patent search where patents are classified into a hierarchy of categories, and the analyses of the experimental results showed not only the positive roles of phrases and words in retrieving additional relevant documents through query expansion but also their contributions to alleviating the query drift problem. More specifically, our query expansion method was compared against relevance-based language model, a state-of-the-art query expansion method, to show its superiority in terms of MAP on all levels of the classification hierarchy. [ABSTRACT FROM AUTHOR]