Use of Macro/Micro Models and Business Intelligence tools for Energy Assessment and Scenario based Modeling

Abstract

Energy assessment and scenario based modeling needs a large quantitative information for providing an accurate analysis. This requires a need of computer model to analyze them. These models (financial, economic, or etc.) often employ scenario analysis to investigate different assumptions about the technical and economical conditions at play. Hence, a large number of software tools, techniques and modeling approaches are used, some better than the other. A wide range of techniques are employed, ranging from broadly economic to broadly engineering. Mathematical optimization is often used to determine the least-cost in some sense. Models can be international, regional, national, municipal, or stand-alone in scope. However, the choice of a suitable model is highly complex since a wrong choice will cost industry a significant amount of time and money only to end up with some inaccurate or wrong results. The initial part of this study analyze different approaches for energy modeling and which software tool will be most feasible to use under those conditions. The outputs from these models may include the system feasibility, greenhouse gas emissions, cumulative financial costs, natural resource use, and energy efficiency of the system under investigation. Hence, even after careful selection of a modeling tool, the interpretation of output data is still a complex task since it requires handling a large amount of data. Thus, the second part of this study deals with business intelligence tools that can be used to handle and properly interpret that data. Especially in the context of scenario based modeling, these tools can provide graphical representation of different dynamics just by using simple clicks once the model is ready. This analysis will help industries and R&D departments through performing better assessments and ease of data handling.