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Assessing the Performance of Residential Energy Management Control Algorithms: Multi-Criteria Decision Making Using the Analytical Hierarchy Process (Revision 1)

Published

Author(s)

Farhad Omar

Abstract

For homes to become active participants in a smart grid, intelligent control algorithms are needed to facilitate autonomous interactions that take homeowner preferences into consideration. Many control algorithms for demand response have been proposed in the literature. Comparing the performance of these algorithms has been difficult because each algorithm makes different assumptions or considers different scenarios, i.e., peak load reduction or minimizing cost in response to the variable price of electricity. This work proposes a flexible assessment framework using the Analytical Hierarchy Process to compare and rank residential energy management control algorithms. The framework is a hybrid mechanism that derives a ranking from a combination of subjective user input representing preferences, and objective data from the algorithm performance related to energy consumption, cost and comfort. The Analytical Hierarchy Process results in a single overall score used to rank the alternatives. The approach is illustrated by applying the assessment process to six residential energy management control algorithms.
Citation
Technical Note (NIST TN) - 2017r1
Report Number
2017r1

Keywords

AHP, Analytical Hierarchy Process, assessment of control algorithms, assessment, assessment and ranking, assessment engine, energy management control algorithms, MADA, MCDM, multi criteria decision making, performance assessment, ranking, residential control algorithms

Citation

Omar, F. (2019), Assessing the Performance of Residential Energy Management Control Algorithms: Multi-Criteria Decision Making Using the Analytical Hierarchy Process (Revision 1), Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2017r1 (Accessed April 23, 2024)
Created June 5, 2019, Updated March 1, 2021