A clustering-based rule-mining approach for monitoring long-term energy use and understanding system behavior

Mirebrahim, Seyed Hamid; Shokoohi-Yekta, Mohammad; Kurup, Unmesh; Welfonder, Torsten; Shah, Mohak

doi:10.1145/3137133.3137144

Cited by 2 publications

(1 citation statement)

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“…Data could also be clustered before extracting the ARs, even if some relationships can be disregarded (e.g., the support of such rules does not reach the minimum support required). In order not to lose any relevant information, in the energy consumption monitoring field, data are often partitioned according to a specific strategy, e.g., in fixed ranges [64]. Hence, in our application, the discretization of the continuous variables is defined in accordance with the O&M department, so that all the different operating conditions can be mapped.…”

Section: Brief Background Of the Ann-dm Techniques Selectedmentioning

confidence: 99%

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

2019

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In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.

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Section: Brief Background Of the Ann-dm Techniques Selectedmentioning

confidence: 99%