A case study to examine the imputation of missing data to improve clustering analysis of building electrical demand

Inman, Daniel; Elmore, Ryan; Bush, Brian

doi:10.1177/0143624415573215

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Different machine learning approaches have been employed for forecasting and backcasting missing demand data due to improper database management systems [36]. The application of clustering and missing data imputation were examined for a large electricitydemand dataset [37]. The mechanisms of missing data are often categorized into the following three types and considered accordingly for imputation: missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) [38].…”

Section: Review Of the State-of-the-art Researchmentioning

confidence: 99%

Integrated Inventory Transshipment and Missing-Data Treatment Using Improved Imputation-Level Adjustment for Efficient Cross-Filling

2022

Sustainability

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This research investigates an integrated problem of transshipment for cross-filling and imputation for missing demand data. Transshipment for cross-filling has proved effective in mitigating shortages with relatively low inventory, thus reducing resource consumption in inventory management. Although accurate demand data are critical for cross-filling decision making, some demand data are inevitably incomplete. These missing data should be treated for effective transshipment operations. Despite the importance, these missing data issues have not been adequately studied for transshipment problems. This paper addresses how transshipment can be conducted under missing demand conditions. A novel integrated problem is established to combine demand-data imputation processes and transshipment decisions. Imputation strategies and new algorithms suitable for transshipment are developed to handle missing demand data. Diverse demand and transshipment cases are analyzed for cost-effectiveness. The analysis uncovers that conventional straightforward imputation methods result in inferior transshipment decisions. The study also reveals that imputed values should be adjusted to appropriate levels for transshipment to be effective. The strong interplay between imputation processes and shortage prevention is also discovered for transshipment with missing demand. This study demonstrates how inventory transshipment can be carried out successfully with appropriate treatment of missing demand data in practice.

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Section: Review Of the State-of-the-art Researchmentioning

confidence: 99%

Integrated Inventory Transshipment and Missing-Data Treatment Using Improved Imputation-Level Adjustment for Efficient Cross-Filling

2022

Sustainability

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“…Inman et al [ 14 ] explored the use of missing data imputation and clustering on building electricity consumption data. The objective was to compare two data imputation methods: Amelia multiple imputation and cubic spline imputation.…”

Section: Introductionmentioning

confidence: 99%

A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems

Zhang

2020

Sensors

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Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.

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“…Kornelsen and Coulibaly [34] demonstrated data-driven approaches against conventional infilling techniques (i.e., the statistical and interpolation infilling approaches) for the imputation of missing values in a distributed soil moisture dataset. Inman et al [35] compared two imputation approaches (the cubic spline and multiple imputations) and two clustering techniques (autocorrelation-based fuzzy clustering and wavelet-based clustering) on the electrical demand data of a commercial building. Nelwamondo et al [36] developed the expectation maximization (EM) algorithm, which was combined with the auto-associative neural network and genetic algorithm (GA), to solve the problem of missing data imputation.…”

Section: Introductionmentioning

confidence: 99%

Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data

Choi

Jung

2020

Sustainability

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Forecasting hydraulic data such as pressure and demand in water distribution system (WDS) is an important task that helps ensure efficient and accurate operations. Despite high-performance data prediction, missing data can still occur, making it difficult to effectively operate WDS. Though the pressure data are directly related to the rules of operation for pumps or valves, few studies have been conducted on pressure data forecasting. This study proposes a new missing and incomplete data control approach based on real pressure data for reliable and efficient WDS operation and maintenance. The proposed approach is: (1) application of source data from high-resolution, real-world pressure data; (2) development of a cross-domain artificial neural network (CDANN), combining the standard artificial neural networks (ANNs) and the cross-domain training approach for missing data control; and (3) analysis of standard data mining according to external factors to improve prediction accuracy. To verify the proposed approach, a real-world network located in South Korea was used, and the forecasting results were evaluated through performance indicators (i.e., overall, special points, and percentage errors). The performance of the CDANN is compared with that of standard ANNs, and CDANN was found to provide better predictions than traditional ANNs.

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A case study to examine the imputation of missing data to improve clustering analysis of building electrical demand

Cited by 12 publications

References 10 publications

Integrated Inventory Transshipment and Missing-Data Treatment Using Improved Imputation-Level Adjustment for Efficient Cross-Filling

Integrated Inventory Transshipment and Missing-Data Treatment Using Improved Imputation-Level Adjustment for Efficient Cross-Filling

A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems

Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data

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