5 Analytical Constructs for Modelling Energy Prices

As IOT Data proliferates one of the game changing use cases which it enables is dynamic pricing. As assets get instrumented one can have usage based pricing of assets on lease. We are already seeing disruptions in pricing model in the automotive industry where sensor data which is a proxy for driving habits is fuelling usage based insurance premiums.

Flutura has been working with Utility companies which is one of the industries in Industrial Internet/IOT category undergoing fundamental shifts because of deregulation and increased instrumentation of the grid. Pricing in the utility industry is a very crucial lever and there is a lot of headroom for Utility companies to innovate on their pricing levers using data science. In this blog Flutura outlines 9 components in the pricing framework  out of which 5 analytical constructs can be used to dig deeper into pricing models. Dig deeper into the science of pricing using big data analytics can impacts multiple Utility outcomes like Profitability, Customer churn (now that Utility is getting deregulated) and Peak grid stress (influencing customers behaviour at peak time using pricing)


Based on the maturity of the market, the intended business outcome and the amount of deregulation in the distribution of energy a number of pricing models can be adopted. One could adopt TOU (Time of use) pricing which charges a premium for energy consumed at peak time. One could adopt UBP (Usage based pricing) model where based on the deviance from the median usage a customer could be charged a premium for the differential energy consumed. The construct to operationalize will have to be carefully chosen based on the factors outlined above. Some of the specific signals governing the choice of model can be the sensitivity of the neighbourhood to price changes, the sentiment of the end consumer to marginal price changes, the kind of segment the the customer belongs to and past knowledge bank of what stimuli worked for the consumer segment. Each of these are elaborated below


Price elasticity process essentially answers a simple question – Is the neighbourhood responsive to price changes? Is Houston more sensitive to the$ 0.25 cent increase in unit energy pricing than Dallas? What is the % change in peak energy consumed in Palo Alto and its neighbourhood when there is a change in peak price? Do households in Austin change their energy consumption pattern in response to time of use pricing (TOU) or Usage based pricing or Location based pricing framework? Depending on the observed behaviour we can be tag that Austin as price sensitive neighbourhood using markers. Also if 2 neighbourhoods have similar characteristics, one can do what if scenario analysis to understand the potential reduction in energy consumed in that neighbourhood as a function of changed pricing

Click here to read full article (the 5 constructs)

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Comment by ajit jaokar on October 10, 2015 at 9:06pm

love it! very good! 

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