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Energy plays a prominent role in human society. As a result of technological and industrial developments, the demand for energy is rapidly increasing. Existing power sources that are mainly fossil fuel based are leaving an unacceptable legacy of waste and pollution apart from diminishing stock of fuels. Hence, the focus is now shifted to large-scale propagation of renewable energy. Renewable energy technologies are clean sources of energy that have a much lower environmental impact than conventional energy technologies. Solar energy is one such renewable energy. Most renewable energy comes either directly or indirectly from the sun. Estimation of solar energy potential of a region requires detailed solar radiation climatology, and it is necessary to collect extensive radiation data of high accuracy covering all climatic zones of the region. In this regard, a decision support system (DSS) would help to estimate solar energy potential considering the regions’ energy requirement. This article explains the design and implementation of DSS for assessment of solar energy. The DSS with executive information systems and reporting tools helps to tap vast data resources and deliver information. The main hypothesis is that this tool can be used to form a core of practical methodology that is resilient and can be used by decision-making bodies to assess various scenarios. It also offers means of entering, accessing and interpreting the information for the purpose of sound decision-making. [ABSTRACT FROM AUTHOR]