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Airborne wind energy (AWE) systems have emerged as cost-effective and sustainable solutions that have not yet been coupled with solar technologies and integrated power plants to produce energy and on-demand substances. This study proposes an integrated system driven by an innovative AWE and photovoltaic (PV) hybrid system. This combination can harness stronger and more stable wind energy while decreasing system costs and power intermittency. The proposed system combines seven subsystems, including AWE, PV, air separation unit, oxyfuel power plant, absorption refrigeration, a nitrogen liquefaction process, and Organic Rankine Cycle (ORC) to simultaneously generate power, liquid nitrogen, and liquid carbon dioxide. The hybrid AWE-PV system can generate 10.8 MWpower to initiate the system to produce 55 MWpower, 127.2 m3/hliquid nitrogen, and 98.4 m3/hliquid carbon dioxide. The exergy analysis has been conducted, showing maximum exergy destruction in heat exchangers, and the total exergy efficiency of the integrated structure reaches 90.21 %. The exergoeconomic analysis illustrates that the maximum capital cost occurs in compressors and turbines with a percentage of 51 % (∼4600 $/h) and 26 % (∼2400 $/h), respectively. This first demonstration of implementing hybrid AWE-PV renewable energy sources in an integrated structure can open new perspectives and avenues toward using AWE and its combination with other renewable energy sources in the future.

Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management, cost savings, environmental sustainability, and responsible energy consumption. Furthermore, short-term heating energy prediction contributes to zero-energy building performance in cold climates. Given the critical importance of short-term forecasting in heating energy management, this study evaluated six prevalent deep-learning algorithms to predict energy load, including single and hybrid models. The overall best-performing predictors were hybrid models using Convolutional Neural Networks, regardless of whether they were multivariate or univariate. Nevertheless, while the multivariate models performed better in the first hour, the univariate models often were more accurate in the final 24 h. Thus, the best-performing predictor of the first timestep was a multivariate hybrid Convolutional Neural Network–Recurrent Neural Network model with a coefficient of determination (R²) of 0.98 and the lowest mean absolute error. Yet, the best-performing predictor of the final timestep was the univariate hybrid model Convolutional Neural Network–Long Short-Term Memory with an R² of 0.80. Also, the prediction accuracy of the best-performing multivariate hybrid models reduced faster per hour compared to the univariate models. These findings suggest that multivariate models may be better suited for early timestep predictions, while univariate models may be better suited for later time steps. Hence, combining the models can enhance accuracy at various timesteps for achieving high fidelity in forecasting and offering a comprehensive tool for energy management.

Higher requirements for buildings' energy performance and indoor environmental quality have prompted new technologies such as latent heat storage with phase change materials capable of storing and releasing significant quantities of heat per unit mass near room temperature. Conventional building materials (e.g., gypsum, concrete) used for mixing with microencapsulated phase change materials (MPCM) often contain high embodied energy. Hempcrete is a sustainable biocomposite material that can significantly reduce a building's embodied energy and energy consumption while enhancing indoor environmental quality. This research aims to develop a new low-carbon latent heat storage material composed of hempcrete and MPCMs with improved hygrothermal properties for sustainable buildings. Eight hempcrete composites were created using different design mixes using hydrated lime, metakaolin, hydraulic lime, and recycled crushed brick. Furthermore, eight hempcrete-MPCM composites were made using two MPCM types, four MPCM melting temperatures, and two MPCM concentrations. The characterization of composites' thermal and moisture properties includes measuring thermal conductivity, volumetric heat capacity, and moisture buffer capacities. The findings suggest that the developed hempcrete-MPCM samples have a higher heat storage capacity than the hempcrete due to their higher volumetric heat capacity. Moreover, hempcrete-MPCM samples have lower thermal conductivity than hempcrete samples in the same density range and testing orientation. The average moisture buffering value for the hempcrete and HPCM samples of 2.78 and 2.76 (gm/m2 RH%), respectively, indicates excellent moisture buffering performance. The results suggest that the optimal integration of MPCMs requires a thorough consideration of the operating temperature and percentage of MPCMs within the hempcrete concerning the specific application and performance objectives.