# Assessment of Solar Energy Generation Toward Net-Zero Energy Buildings
###### Abstract
[20] has utilized machine learning tools to develop a PV output prediction model that includes data quality checks, a machine learning algorithm, weather clustering, and accuracy assessments, resulting in enhanced prediction capabilities when linear regression coefficients are applied. Despite advances in forecasting accuracy, precisely predicting the PV output under constantly changing weather conditions remains impossible, leading to operational complexities in NZEBs. Numerous studies have tackled the optimal operation of NZEBs under such uncertainties [21]. For instance, the authors in ref. [22] have developed a framework combining multi-objective optimization with robustness analysis to design integrated building energy systems, employing two-stage stochastic programming for balancing economic and environmental goals while validating robustness through Monte Carlo simulations. The authors in ref. [7] have developed a multi-objective optimization process aiming to minimize the operating GHG emissions and the life-cycle cost. The process has been applied to a typical multi-residential building and tested in the four Greek climate zones. Although existing research studies [19; 20; 22; 23] have significantly focused on enhancing the accuracy of prediction models or optimizing NZEB operations, more studies are still required to develop comprehensive operational frameworks for building energy management systems with the necessary features. Additionally, developing a user-friendly graphical user interface (GUI) that provides real-time visualization of system status and integrates both predictive and optimization functionalities could greatly assist building operators in managing and controlling building energy systems more effectively. To address these multifaceted challenges, this study proposes an optimization framework specifically designed for NZEBs. The framework comprises two primary phases:accurately predicting renewable energy outputs, especially from PV systems, and strategically optimizing energy production and consumption. The initial phase utilizes neural network (NN)-based prediction models to forecast the output power of the PV system accurately, taking into account a range of environmental and technical factors. The subsequent phase focuses on optimizing building systems for cooling, heating, and electricity generation using mixed integer linear programming (MILP), aiming to minimize operational costs and maximize efficiency. This approach not only enhances the reliability and performance of NZEBs but also reduces their dependence on external power sources, thereby bolstering the sustainability of building operations. The integration of this model promotes PV as an energy source for buildings, further providing a sustainable approach to powering buildings. The increased use of renewable energy sources like PV will help reduce harmful environmental impacts. This approach provides a feasible environmental solution because it shows building operators that their buildings can efficiently operate without the need for nonrenewable energy as the core energy provider. Additionally, the implementation of a GUI facilitates intuitive and user-friendly interaction with the system, enabling building operators to manage and optimize their energy resources efficiently. The major contributions of this study are listed as follows:  
* An operation strategy is proposed for NZEBs, including deep neural network-based renewable energy output predictions and a MILP-based optimization for multi-energy balance. * Reliability and operational efficiency are significantly enhanced by accurately forecasting PV output and optimizing building systems for cooling, heating, and electricity, thus reducing dependency on external systems. * A user-friendly GUI is developed for building operators, enabling effective energy resource management and optimizing energy use in real time.