Automated Load Curve Data Cleansing in Power Systems
TLDR
The B-Spline smoothing and Kernel smoothing based techniques to automatically cleanse corrupted and missing data are presented and a man-machine dialogue procedure is proposed to enhance the performance.Abstract:
Load curve data refers to the electric energy consumption recorded by meters at certain time intervals at delivery points or end user points, and contains vital information for day-to-day operations, system analysis, system visualization, system reliability performance, energy saving and adequacy in system planning. Unfortunately, it is unavoidable that load curves contain corrupted data and missing data due to various random failure factors in meters and transfer processes. This paper presents the B-Spline smoothing and Kernel smoothing based techniques to automatically cleanse corrupted and missing data. In implementation, a man-machine dialogue procedure is proposed to enhance the performance. The experiment results on the real British Columbia Transmission Corporation (BCTC) load curve data demonstrated the effectiveness of the presented solution.read more
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References
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Book
Applied Linear Statistical Models
TL;DR: Applied Linear Statistical Models 5e as discussed by the authors is the leading authoritative text and reference on statistical modeling, which includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half.
Book
A practical guide to splines
TL;DR: This book presents those parts of the theory which are especially useful in calculations and stresses the representation of splines as linear combinations of B-splines as well as specific approximation methods, interpolation, smoothing and least-squares approximation, the solution of an ordinary differential equation by collocation, curve fitting, and surface fitting.
Journal ArticleDOI
Applied Linear Statistical Models
Journal ArticleDOI
Time series analysis, forecasting and control
P. Young,S. Shellswell +1 more
TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.
Book
The Analysis of Time Series: An Introduction
TL;DR: In this paper, simple descriptive techniques for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.