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Thermal comfort is an essential component of smart cities that helps to upgrade, analyze, and realize intelligent buildings. It strongly affects human psychological and physiological levels. Residents of buildings suffer stress because of poor thermal comfort. Buildings frequently use Heating, Ventilation, and Air Conditioning (HVAC) systems for temperature control. Better thermal states directly impact people’s productivity and health. This study revealed a human thermal comfortmodel thatmakes better predictions of thermal sensation by identifying essential features and employing a tuned Extra Tree classifier, MultiLayer Perceptron (MLP) and Naive Bayes (NB) models. The study employs the ASHRAE RP-884 standard dataset for experimentation and analysis, which is available to the public. ExploratoryData Analysis (EDA) is performed to examine the outliers and anomalies in the dataset. The SyntheticMinorityOver-Sampling Technique (SMOTE) enhances theminority class’s interpretation. The proposed Extra Tree classifier outperforms by achieving an accuracy of 94%. The experiment shows that the suggestedmodel is superior to other established methods and state-of-the-art. [ABSTRACT FROM AUTHOR]

To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources, has been carried out. This has been done using a new meta-heuristic algorithm, improved artificial rabbits optimization (IARO). In this study, the uncertainty of solar and wind energy sources is modeled using Hang's two-point estimating method (TPEM). The IARO algorithm is applied to calculate the best capacity of hub energy equipment, such as solar and wind renewable energy sources, combined heat and power (CHP) systems, steamboilers, energy storage, and electric cars in the day-ahead market. The standard ARO algorithm is developed to mimic the foraging behavior of rabbits, and in this work, the algorithm's effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique. The proposed IARO-based scheduling framework's performance is evaluated against that of traditional ARO, particle swarm optimization (PSO), and salp swarm algorithm (SSA). The findings show that, in comparison to previous approaches, the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity, gas, and heating markets by satisfying the operational and energy hub limitations. Additionally, the results show that TPEM approach dependability consideration decreased hub energy's profit by 8.995% as compared to deterministic planning. [ABSTRACT FROM AUTHOR]

Saudi Arabia generates more than 98% of its electricity through hydrocarbon resources. To reduce the consumption of fossil fuel resources and protect the environment, the government of Saudi Arabia is planning to make renewable energy an essential part of its energy mix. In this study, due to the country's abundant solar potential, solar energy has been selected as the energy source to generate renewable energy in Saudi Arabia. The two solar energy technologies, photovoltaic (PV) and solar thermal, have been analyzed in three different locations within the country. Multi-criteria decision-making (MCDM) techniques were used to rank the cities for each of the technologies. The SAW(Simple Additive Weighting)-AHP(Analytic Hierarchy Process) MCDM method based on climate, environmental, technical, economic, and social has been adopted to analyze the suitability of each technology for all locations. To assign weights to the criteria AHP method was used, while to rank the technologies, SAW was used. The results show that for the PV technology, Abha ranked 1st with a performance score of 91%, making it the most suitable location, followed by Jeddah with 83%. While for solar thermal technologies, Jeddah is the most suitable location, with a performance score of 96%, followed by Abha with 91%. The PV systems generated a maximum of 11,019 MWh in Abha, while the solar thermal produced maximum of 14,000 MWh in Jeddah. Overall, solar thermal technology outperformed PV technology in Saudi Arabia due to the country's higher temperature. The analysis of photovoltaic and solar thermal technologies in this study provides valuable insight for the government of Saudi Arabia in identifying the best site for solar energy technologies in the country. [ABSTRACT FROM AUTHOR]

The low bandgap antimony selenide (Sb2Se3) and wide bandgap organic solar cell (OSC) can be considered suitable bottom and top subcells for use in tandem solar cells. Some properties of these complementary candidates are their non-toxicity and cost-affordability. In this current simulation study, a two-terminal organic/Sb2Se3 thin-film tandem is proposed and designed through TCAD device simulations. To validate the device simulator platform, two solar cells were selected for tandem design, and their experimental data were chosen for calibrating the models and parameters utilized in the simulations. The initial OSC has an active blend layer, whose optical bandgap is 1.72 eV, while the initial Sb2Se3 cell has a bandgap energy of 1.23 eV. The structures of the initial standalone top and bottom cells are ITO/PEDOT:PSS/DR3TSBDT:PC71BM/PFN/Al, and FTO/CdS/Sb2Se3/Spiro-OMeTAD/Au, while the recorded efficiencies of these individual cells are about 9.45% and 7.89%, respectively. The selected OSC employs polymer-based carrier transport layers, specifically PEDOT:PSS, an inherently conductive polymer, as an HTL, and PFN, a semiconducting polymer, as an ETL. The simulation is performed on the connected initial cells for two cases. The first case is for inverted (p-i-n)/(p-i-n) cells and the second is for the conventional (n-i-p)/(n-i-p) configuration. Both tandems are investigated in terms of the most important layer materials and parameters. After designing the current matching condition, the tandem PCEs are boosted to 21.52% and 19.14% for the inverted and conventional tandem cells, respectively. All TCAD device simulations are made by employing the Atlas device simulator given an illumination of AM1.5G (100 mW/cm2). This present study can offer design principles and valuable suggestions for eco-friendly solar cells made entirely of thin films, which can achieve flexibility for prospective use in wearable electronics. [ABSTRACT FROM AUTHOR]