Omar, F.
, Bushby, S.
and Williams, R.
(2018),
Assessing the Performance of Residential Energy Management Control Algorithms: Multi-Criteria Decision Making Using the Analytical Hierarchy Process, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2017
(Accessed November 21, 2024)
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Assessing the Performance of Residential Energy Management Control Algorithms: Multi-Criteria Decision Making Using the Analytical Hierarchy Process
Published
Author(s)
, , Ronald D. Williams
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 object 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) - 2017
Report Number
2017
NIST Pub Series
Pub Type
NIST Pubs
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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
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Created September 18, 2018, Updated November 10, 2018