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Rising energy demand, resource depletion, and environmental issues tied to fossil fuels demand a transition to renewable energy. Solar power, abundant and well-established, presents a promising solution to address our expanding energy requirements. The sun radiates an astonishing amount of energy every second, far more than humanity’s current and future energy needs. Accurate solar radiation prediction is crucial for optimizing solar panel design, placement, and grid integration. This paper aims to predict daily global solar radiation data for six Pakistani cities: Karachi, Lahore, Islamabad, Quetta, Peshawar, and Multan. It highlights the importance of advanced algorithms and introduces an innovative data collection method using pyranometer sensors and microcontrollers, making data storage and analysis more affordable and efficient while reducing the financial burdens associated with traditional equipment. Focusing on Pakistan’s diverse solar radiation potential, this research evaluates eight machine learning algorithms using seven key statistical metrics to understand and compare their performance in predicting solar radiation. Four algorithms, k-nearest neighbors, Random Forest Regression, Gradient Boosting Regression, and Support Vector Regression (SVR), consistently exhibit remarkable precision, achieving outstanding R2 values of up to 99%. This highlights the crucial role of algorithm selection in solar radiation prediction, with SVR emerging as the top choice. SVR’s precise and reliable forecasts empower renewable energy planning and decision-making. This study provides valuable guidance for decision-makers to optimize solar energy utilization across diverse geographical regions and contributes invaluable insights to the field of renewable energy forecasting.

Building is a process of technology that enables human wellbeing by engaging a multitude of stakeholders. However, it is often seen in the literature that all stakeholders involved in the building process are disintegrated, which seems to be a management and systemic orientation problem when trying to address the built environment as a sustainable system design. That means the design and design aesthetics of any building cannot be simply influenced by technological revolution and efficiency improvements to attain the three pillars of sustainable development. A systemic transformation of the whole process is a pre-requisite, which is complex and oft underestimated mainly due to the lack of subjective characteristics involved available for transformation research, that addresses policy choice in the development of a paradigm shift. Addressing built environment as a system for transformation research practice, this study extensively reviewed the UNEP built environment report to identify global variables that define the roughly correct system as an artifact to be studied. The UNEP report covers the system level transformation requirement until 2003, which seems to be more or less the same in almost all the research studies as of now. Using the Vester Sensitivity Model, the roughly correct built environment as a system is analyzed to identify the future leverage potential and variables’ interdependency. The in-depth systems analysis indicates global variables Enabling condition and New material are potential system leveraging variables for built environment system transition to sustainable design, which require further scrutiny. The leveraging variables are influenced by other global variables which can only be negotiated for sustainable development goal actualization for future success.