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Load Forecasting Tool for NIST Transactive Energy Market

Published

Author(s)

Farhad Omar, David Holmberg

Abstract

Customers and transactive energy (TE) market managers may rely on load forecasting algorithms to purchase or sell power in a forward market environment, using day-ahead and real-time pricing structures. Accurate load forecasting becomes necessary when a local controller or aggregator interacts with a market to purchase energy for future use. This study introduces a load forecasting tool (LFT) that estimates the next-day energy consumption of residential house models in GridLAB D. The LFT is an integral part of the National Institute of Standards and Technology (NIST) TE simulation testbed which provides a platform for conducting TE experiments. The LFT is comprised of two main components, a learning algorithm and a load forecasting algorithm utilizing a first-order lumped capacitance model to forecast the next day indoor temperature and energy consumption. The lumped capacitance model simulates the thermal characteristics of a residential house in response to heat gains or losses due to the heat pump operation and other environmental conditions, such as outdoor air temperature and solar irradiance. The learning algorithm uses simulated indoor temperature from GridLAB-D and historical weather data for Tucson Arizona to estimate critical parameters of a residential house such as thermal time constant, solar heat gain coefficient, effective window area, and the heat pump coefficient of performance. The load forecasting algorithm utilizes these parameters to optimize the operation of a residential heat pump while minimizing cost and maintaining thermal comfort. The load forecasting algorithm resulted in average energy savings of 9.4 % and average cost savings of 19.4 % compared to simulated baseline energy consumption in GridLAB-D. The LFT's forecast temperature and energy consumption profiles have been integrated into a co-simulation experiment for validation.
Citation
Technical Note (NIST TN) - 2181
Report Number
2181

Keywords

Energy use forecasting, heat pump controller, heat pump energy consumption, load forecasting, lumped capacitance model, multi-objective optimization, optimization algorithm, overall heat transfer conductance, parameter estimation, parameter learning, parameter optimization, solar heat gain, thermal model, thermal time constant, transactive energy.

Citation

Omar, F. and Holmberg, D. (2021), Load Forecasting Tool for NIST Transactive Energy Market, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2181, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933235 (Accessed December 4, 2021)
Created September 27, 2021