A holistic approach to presenting DSM project results

A slight variance in dataset quality or baseline model accuracy can affect the level of confidence at which DSM project results are calculated. General measurement and verification (M&V) guidelines mitigate this inherent variance by reporting a conservative result. However, it remains important to quantify the magnitude of variance to ensure that the conservative approach does not adversely affect any of the stakeholders involved. A critical analysis of published literature relating to the M&V of DSM projects highlights a strong tendency towards presenting project impact at a high level of accuracy. Published results are generally presented without interpretation and therefore do not objectively convey the true nature of system performance. The analysis also identified a lack of long-term evaluation techniques which can be applied to continuously monitor project performance. This prompts the development of a holistic approach to presenting DSM project results. This paper utilises graphical presentation to clearly convey project performance characteristics. The methodologies presented in the paper are applied to several industrial DSM case studies in order to verify the practical application in real life scenarios. The results highlight previously unknown characteristics which can significantly influence how stakeholders perceive project success. The results are further validated by comparing case study results to results obtained from independent 3^rd parties.