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Thermal profiling of residential energy use

Stanford engineers have developed a method for identifying modeling and estimating energy usage patterns. A utility provider may use this data as an incentive for consumers to shift energy consumption away from peak hours. In recent years energy utility companies have rolled out advanced infrastructures while smart meters collect detailed data about users’ energy consumption. Using smart meter and weather data the Stanford model identifies those consumers whose variable usage would be most effective in balancing peak supply and demand. Since heating and air conditioning make up about 25% of electricity use in the U.S. it is key to understand how much energy consumed by each customer is temperature sensitive. The statistical model quantifies the thermal and non-thermal components for individual residential users. The thermal response description allows for comparison between users for Demand-Response (DR) programs allowing scarce budgets to focus on reaching those consumers in which behavioral change will result in the highest energy reductions at the right time. Segmentation and targeting of users may offer savings exceeding 100% of a random strategy. For 1 million homes controlled by a DR program the potential savings could be 250 megawatts of capacity for every degree Fahrenheit of air conditioning. Applications: Energy demand management - data analytics of high resolution temporal energy consumption for Demand Response Programs targeting select customers that are most likely to adequately balance demand based on forecast temperature


1) Data driven with low computational cost - The model allows the most detailed description to date of individual energy consumption in a way that accounts for the large degree of volatility seen in residential use. 2) Flexible and Scalable - The model derives rich benchmarks and metrics that quickly and succinctly describe complex features of the data. Model can incorporate physical thermal and occupancy states of the premise building profiles of occupancy as reflected in the magnitude variance and dynamics of the consumption process.

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