Coordinated integration of distributed energy resources in unit commitment

Munawar, Usman; Wang, Zhanle

doi:10.1016/j.ijepes.2022.108671

Cited by 3 publications

(1 citation statement)

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“…The machine learning paradigm has been widely applied to solve energy efficiency problems [51][52][53][54]. This type of paradigm allows for the input of a series of data and the execution of processes that result in an output.…”

Section: Machine Learning Modulementioning

confidence: 99%

TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

2023

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Pre-processing and analysis of sensor data present several challenges due to their increasingly complex structure and lack of consistency. In this paper, we present TSxtend, a software tool that allows non-programmers to transform, clean, and analyze temporal sensor data by defining and executing process workflows in a declarative language. TSxtend integrates several existing techniques for temporal data partitioning, cleaning, and imputation, along with state-of-the-art machine learning algorithms for prediction and tools for experiment definition and tracking. Moreover, the modular architecture of the tool facilitates the incorporation of additional methods. The examples presented in this paper using the ASHRAE Great Energy Predictor dataset show that TSxtend is particularly effective to analyze energy data.

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Section: Machine Learning Modulementioning

confidence: 99%