Missing data solution of electricity consumption based on Lagrange Interpolation case study: IntelligEnSia data monitoring

Manembu, Pinrolinvic; Kewo, Angreine; Welang, Brammy

doi:10.1109/iceei.2015.7352554

Cited by 18 publications

(9 citation statements)

References 7 publications

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“…In data collection and pre-processing phases, it is important to study the latter comprehensively, as it can improve data quality and the accuracy of the result [52]. Data corruption, missing values and outliers are the commonest problems in data-processing [52,53]. In general, there are four tasks in data pre-processing: cleaning, transformation, integration and reduction [52,54,55].…”

Section: Methodsmentioning

confidence: 99%

Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

2020

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It is important to understand residential energy use as it is a large energy consumption sector and the potential for change is of great importance for global energy sustainability. A large energy-saving potential and emission reduction potential can be achieved, among others, by understanding energy consumption patterns in more detail. However, existing studies show that it requires many input parameters or disaggregated individual end-uses input data to generate the load profiles. Therefore, we have developed a simplified approach, called weighted proportion (Wepro) model, to synthesise the residential electricity load profile by proportionally matching the city’s main characteristics: Age group, labour force and gender structure with the representative households profiles provided in the load profile generator. The findings indicate that the synthetic load profiles can represent the local electricity consumption characteristics in the case city of Amsterdam based on time variation analyses. The approach is in particular advantageous to tackle the drawbacks of the existing studies and the standard load model used by the utilities. Furthermore, the model is found to be more efficient in the computational process of the residential sector’s load profiles, given the number of households in the city that is represented in the local profile.

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Section: Methodsmentioning

confidence: 99%

Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

2020

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“…Cleaning, transformation, integration and reduction are the common techniques of data pre-processing in statistical analysis. The cleaning task will correct the data by filling in the missing values into a data warehouse or correct the dataset by using specific techniques, such as interpolation [24], smoothing noise data, identifying or removing outliers and resolving duplicates and inconsistencies, while minimising an introduction of bias into the data. The transformation translates, converts and/or scales the variables into the required or desired formats or units.…”

Section: Methodsmentioning

confidence: 99%

“…This solution has been proposed for pre-processing by employing the interpolation technique, called Lagrange interpolation. It is a reformulation of the Newton polynomial to avoid the computation of divided differences [24].…”

Section: Related Workmentioning

confidence: 99%

Data Pre-processing Techniques in the Regional Emission's Load Profiles Case

Kewo

Manembu

Nielsen

2019

2019 6th International Conference on Control, Decision and Information Technologies (CoDIT)

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Different data sources, data types and platforms are involved in modelling emissions load profiles. In our case, we model emission load profiles at the regional or city level. However, we found missing values, redundancy and inconsistency in the datasets, and in most cases data preprocessing is unavoidable. Data pre-processing converts the data into a clean and tidy dataset for the subsequent modelling steps or statistical analyses. Therefore, some common techniques for data pre-processing such as cleaning, transformation, integration, reduction and some terms in data mining such as filtering and selection have been applied in our case study. We usually do the data pre-processing of moderate problems and a small amount of data using a spreadsheet application, whereas we use the programming language to do the more complex and big data size tasks. As a result, it has been found that understanding the nature of our data collection, the data flow process and the desired output comprehensively is the key for efficiency in data pre-processing. The applied techniques have helped us to provide the proper input for modelling the regional emission load profile efficiently.

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“…Lagrange interpolating polynomial is described in [4], [15], [16] and can be represented as follows-…”

Section: Fig 6 Input and Output From Foh B Lagrange Polynomial Methodsmentioning

confidence: 99%

Methods of Missing Data Handlingin One Shot Response based Power System Control

Dahal¹

2019

IJEAT

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The work done in this paper addresses various methods of handling missing phasor samples obtained from power flow simulations using DSA tools like TSAT and PSAT. Pseudorandom numbers in MATLAB are used to simulate 0-10% of missing samples and are recovered using different extrapolation techniques. After recovery, samples are subjected to decision trees to assess the performance of one shot stabilizing controls like in [1], [2].The power system model used is the 176 bus model of Western Electrical Coordinating Council (WECC).

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Missing data solution of electricity consumption based on Lagrange Interpolation case study: IntelligEnSia data monitoring

Cited by 18 publications

References 7 publications

Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

Data Pre-processing Techniques in the Regional Emission's Load Profiles Case

Methods of Missing Data Handlingin One Shot Response based Power System Control

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