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This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses with one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. These elements improve energy efficiency and promote sustainability. Operating in island mode, CSGBs can function independently of the grid, providing resilience during power outages and reducing reliance on external energy sources. Real data on electricity, gas, and water consumption are used to optimize load management under isolated conditions. Electric Vehicles (EVs) are also considered in the system. They serve as energy storage devices and, through Vehicle-to-Grid (V2G) technology, can supply power when needed. A hybrid Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. The metrics considered include accuracy, efficiency, emissions, and cost. The performance was compared with several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), and hybrid LSTM–CNN, and the results show that the proposed model provides the best results. For a four-bedroom Connected Smart Green Townhouse (CSGT), the Mean Absolute Percentage Error (MAPE) is 4.43%, the Root Mean Square Error (RMSE) is 3.49 kWh, the Mean Absolute Error (MAE) is 3.06 kWh, and R 2 is 0.81. These results indicate that the proposed model provides robust load optimization, particularly in island mode, and highlight the potential of CSGBs for sustainable urban living. [ABSTRACT FROM AUTHOR]

This paper examines Connected Smart Green Townhouses (CSGTs) as a modern residential building model in Burnaby, British Columbia (BC). This model incorporates a wide range of sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. The CSGTs operate in grid-connected mode to balance on-site renewables with grid resources to improve efficiency, cost-effectiveness, and sustainability. Real datasets are used to optimize resource consumption, including electricity, gas, and water. Renewable Energy Sources (RESs), such as PV systems, are integrated with smart grid technology. This creates an effective framework for managing energy consumption. The accuracy, efficiency, emissions, and cost are metrics used to evaluate CSGT performance. CSGTs with one to four bedrooms are investigated considering water systems and party walls. A deep Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. In particular, the Mean Absolute Percentage Error (MAPE) is below 5%, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are within acceptable levels, and R 2 is consistently above 0.85. The proposed model outperforms other models such as Linear Regression (LR), CNN, LSTM, Random Forest (RF), and Gradient Boosting (GB) for all bedroom configurations. [ABSTRACT FROM AUTHOR]

This study aims to look into the temperature distribution on a vertical flat plate with a variable temperature boundary condition. As a novelty, the variable temperature is considered on the wall, and coupled momentum and energy equation are solved. Moreover, a novel variable change transforms the infinite boundary condition into the finite one. The partial differential governing equations were introduced and transformed into ordinary differential equations form using the similarity solution. The obtained equations were numerically solved and validated using previous research. The results showed that for a constant variable temperature index (n), increasing the Prandtl number (Pr) from 0.1 to 2 reduces the dimensionless maximum velocity by less than half and the skin friction coefficient by about 32%. In this case, the dimensionless temperature approaches zero faster; as a result, the thermal boundary layer thickness declines, and the Nusselt number (Nu) rises. Furthermore, for a constant Pr, when n increases from 0 to 1.5, the dimensionless maximum velocity and the skin friction decrease by about 38% and 23%, respectively. Since the dimensionless temperature continues to descending trend, Nu still rises in this case. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the thermal distribution and heat transfer in the boundary layer of a wedge with a variable surface temperature in the presence of a magnetic field. To achieve this, we first used similarity solutions to transform the governing equations of magnetohydrodynamic flow for variable surface temperature conditions into ordinary differential equations. We then solved the resulting equations using the collocation method (CM) with different intensity magnetic fields and varying Hartmann (Ha) numbers and surface temperatures. The CM was further modified by incorporating boundary conditions. The results obtained from the solved equations were validated and compared with those obtained using the numerical Runge–Kutta fourth‐order method and previous literature. Finally, we investigated the impact of various parameters on the friction coefficient (Cf) and Nusselt number (Nu), including the power of variable surface temperature (n), Prandtl (Pr) number, Eckert (Ec) number, the half angle of the wedge (φ), and Ha number. We considered values for these parameters within the ranges 0.5 ≤ n ≤ 1.5, 0.5 ≤ Pr ≤ 5, 0.001 ≤ Ec ≤ 0.002, 15° ≤ φ ≤ 60°, and 0 ≤ Ha ≤ 3. Our findings indicate that the slope of the boundary layer increases with increasing Ha or φ, resulting in an increase in Cf on the surface by up to 526%. The Nu number, calculated using the energy equation, increases up to 91.7%, 39.8%, and 1.43% with increasing Ha, n, and Ec, respectively, resulting in faster growth of the thermal boundary layer, which causes the thickness to decrease and the Nu number to rise. However, as φ increases, the Nu number drops on the surface, and the heat transfer behavior remains similar to that observed previously. [ABSTRACT FROM AUTHOR]