Demand Forecasting in Smart Grids

Piotr Mirowski, Sining Chen, Tin Kam Ho, and Chun-Nam Yu

Bell Labs Technical Journal, vol. 18, issue 4, 2014.

Data analytics in smart grids can be leveraged to channel the data downpour from individual meters into knowledge valuable to electric power utilities and end-consumers. Short-term load forecasting (STLF) can address issues vital to a utility but it has traditionally been done mostly at system (city or
country) level. In this case study, we exploit rich, multi-year, and highfrequency annotated data collected via a metering infrastructure to perform STLF on aggregates of power meters in a mid-sized city. For smart meter aggregates complemented with geo-specifi c weather data, we benchmark several state-of-the-art forecasting algorithms, including kernel methods for nonlinear regression, seasonal and temperature-adjusted auto-regressive models, exponential smoothing and state-space models. We show how STLF accuracy improves at larger meter aggregation (at feeder, substation, and system-wide level). We provide an overview of our algorithms for load prediction and discuss system performance issues that impact real time STLF.

Paper link: Wiley.