Effects of Multicollinearity on Electricity Consumption Forecasting using Partial Least Squares Regression

Kemalbay, Gülder; Korkmazoğlu, Özlem Berak

doi:10.1016/j.sbspro.2012.09.197

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…In other words, there may be hidden traits within historical power values such as weather or seasonality factors which affect any external variable, like dynamic population, to cause a drastic change within the forecast model. This could also mean that there is multicollinearity [12] between any predictor variables. To detect against and prevent this, applying Variance Inflation Factors (VIF) would be crucial.…”

Section: Interpreting the Accuracy Of Forecast Modelsmentioning

confidence: 99%

Urban Electric Load Forecasting with Mobile Phone Location Data

Selvarajoo

Schläpfer²,

Tan

2018

2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT)

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In recent years, electrical load forecasting has received continuous research efforts aiming to improve the shortterm prediction accuracy of local energy demands. However, current methods are not able to take explicitly into account the dynamic spatial population distribution over the course of a day, which is particularly relevant in dense urban areas. In this paper, we harness society-wide mobile phone data to map the timevarying population distribution in the Trentino region, Italy, and to use these insights for a novel electrical load forecasting method. Our results demonstrate that the integration of aggregated mobile phone data yields compelling forecast models.

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Section: Interpreting the Accuracy Of Forecast Modelsmentioning

confidence: 99%

Urban Electric Load Forecasting with Mobile Phone Location Data

Selvarajoo

Schläpfer²,

Tan

2018

2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT)

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“…The absence of multicollinearity is a mandatory criterion that must be met in all linear regression models. [9][10][11] Another advantage is that the optimized PLS model can be reduced to a common multiple linear regression, allowing to predict the importance of each independent variable in predicting the concentration of the substance of interest. [12][13][14] Thus, the aim of this study was to propose a new mid-infrared and chemometric method for the simultaneous quantification of NTX and BUP.…”

Section: Introductionmentioning

confidence: 99%

A Mid-Infrared Spectrophotometric Method for Simultaneous Quantification of Naltrexone and Bupropion with Multivariate Calibration

Novack¹,

Cobre²,

Madeira³

et al. 2023

J. Braz. Chem. Soc.

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Naltrexone (NTX) and bupropion (BUP) are used in combination in clinical practice for obesity; however, the existing analytical methods for this drug combination are not sustainable. This work aims to develop an analytical method that is faster, easier and less expensive compared with high performance liquid chromatography (HPLC) and involves green technology without solvents to quantify BUP and NTX in combination. The objective was to validate a method using mid-infrared (MIR) spectroscopy associated with multivariate calibration and chemometrics to simultaneously measure NTX and BUP in a pharmaceutical capsule form. The models were developed using MIR spectroscopy with diffuse reflectance, with interval partial least squares (iPLS) variable selection. The working range selected to optimize the model was from 1885.8 to 1585.4 cm-1. The chosen model was obtained with partial least squares (PLS2) and with data pre-processed by first derivative Savitzky-Golay smoothing followed by mean centering, using four latent variables, providing a root mean square error of prediction of 1.8 mg g-1 for NTX and 6.42 mg g-1 for BUP. The method was validated according to current international standards. In conclusion, the methods developed in the MIR region provided statistically similar results to the validated chromatographic method for commercial pharmaceutical formulations.

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“…But majority of the studies in transportation engineering that uses multiple linear regression did not pay much attention to this multicollinearity problem. Studies in others fields like water resources engineering [19,20], electrical engineering [21] and tourism management [22] reported various methods like ridge regression, partial least squares regression and principal component regression to overcome the multicollinearity problem. In this study, we proposed a simple method of defining new variables as a linear combination and ratio of existing independent variables, which not only remove the multicollinearity problem but also help to achieve high R 2 value and significant p values.…”

Section: Introductionmentioning

confidence: 99%

Dealing with Multicollinearity Problem in Analysis of Side Friction Characteristics Under Urban Heterogeneous Traffic Conditions

Singh

Kumar

2021

Arab J Sci Eng

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Disturbance to the moving traffic, such as on-street parking and bus stop on carriageway normally called as side friction in the literature, is one of the major problems that impact the vehicular speed on urban arterials in developing countries like India. Regression model to study the mode-wise vehicular speeds using side friction and other influencing variables faces the problem of multicollinearity as there is a strong correlation between the independent variables which results in high R2 and high p values. Instead of dropping the highly correlated variables which is the conventional practice to deal with multicollinearity, we proposed an approach of taking a linear combination and ratio of independent variables. To test the applicability of the proposed approach, traffic data from five locations in Hyderabad and Vellore in India having a wide variety of geometric and traffic characteristics with one or more side frictions were collected and regression models were built. It was found that the proposed approach of taking linear combination and ratio worked very well as the models exhibited a high R2, low p values and low variance inflation factor. The regression models also forecast speeds with good and reasonable accuracy. For better understanding of the impact of side friction, the present study proposed an index called side friction index and it was found that the number of side friction elements and their impact are directly proportional. Also, the impact is not uniform across the different vehicle types. Though the present study focussed on side friction problem, the approach that we proposed in this paper to deal with multicollinearity can be applied in other fields as well.

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Effects of Multicollinearity on Electricity Consumption Forecasting using Partial Least Squares Regression

Cited by 13 publications

References 14 publications

Urban Electric Load Forecasting with Mobile Phone Location Data

Urban Electric Load Forecasting with Mobile Phone Location Data

A Mid-Infrared Spectrophotometric Method for Simultaneous Quantification of Naltrexone and Bupropion with Multivariate Calibration

Dealing with Multicollinearity Problem in Analysis of Side Friction Characteristics Under Urban Heterogeneous Traffic Conditions

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