Correlation and instance based feature selection for electricity load forecasting

Koprinska, Irena; Rana, Mashud; Agelidis, Vassilios G.

doi:10.1016/j.knosys.2015.02.017

Cited by 195 publications

(100 citation statements)

References 34 publications

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“…Correlation-Based Selection (CFS) method is used in the literature for different purposes. CFS is used to forecast the electricity demand in Australia with linear regression, tree based models, and neural networks prediction algorithms using two years of time series load data [16]. FAST algorithm was proposed by authors used the concept of Correlation Coefficient and Symmetrical Uncertainty to get the best subset.…”

Section: Related Studymentioning

confidence: 99%

A Novel Clustering Based Candidate Feature Selection Framework Using Correlation Coefficient for Improving Classification Performance

Potharaju¹,

Sreedevi²

2017

JESTR

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Feature Selection (FS) is an imperative issue in data mining and machine learning. It is an inevitable task to shorter the number of features presented in the initial data set for better classification result, minimized computation time, and reduced memory consumption. In this article, a novel framework using Correlation Coefficient (CCE) and Symmetrical Uncertainty (SU) for selecting the subset of feature is proposed. The selected features are congregated into finite number of clusters by grading their CCE and comparing the SU values. In each cluster, a feature with maximum SU value is retained while remaining features in the same cluster are ignored. The proposed framework was examined with Ten(10) real time benchmark data sets. Experimental outcomes show that the proposed method is outruns than majority of conventional feature selection methods(Information Gain, Chi-Square, Gain Ratio, ReliefF) in accuracy. This method is tested using Tree Based, Rule Based, Lazy, and Naive Bayes learners.

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Section: Related Studymentioning

confidence: 99%

A Novel Clustering Based Candidate Feature Selection Framework Using Correlation Coefficient for Improving Classification Performance

Potharaju¹,

Sreedevi²

2017

JESTR

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“…At the same time, if the fitness is better than the average fitness of each particle, the corresponding inertia weight is smaller, so this particle can stay. The adjustment equation is given below: (4) where w max is the maximum inertia weight and w min is the minimum inertia weight, f min is the minimum fitness, f avg is the average fitness, and f is the fitness.…”

Section: Self-adaptive Inertia Weight Particle Swarm Optimization (Samentioning

confidence: 99%

“…Electrical load forecasting means to estimate and forecast the electricity demand through analysing and researching the historical data and extracting the inner relationship of data from the perspective of known economic and social development and the demands of the electrical system, considering factors such as politics, economy and climate. In recent years, large-scale power outages in large-scale areas have been caused by extra electrical load, resulting in great economic losses [4]. Thus, the scientific control of electrical load seems vital.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

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Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

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“…A wrapper method for feature selection in ANN was introduced by Xiao et al [60] and Neupane et al [61], a method also adopted by Kang et al [62]. A Gray model ANN and a correlation based feature selection with ANN are given in [63,64]. The Extreme Learning Machine (ELM), a feed-forward NN was used in the works [65,66] and a genetic algorithm, GA, and improved BP-ANN was used in [67].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market

et al. 2016

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Abstract:In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004-2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

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Correlation and instance based feature selection for electricity load forecasting

Cited by 195 publications

References 34 publications

A Novel Clustering Based Candidate Feature Selection Framework Using Correlation Coefficient for Improving Classification Performance

A Novel Clustering Based Candidate Feature Selection Framework Using Correlation Coefficient for Improving Classification Performance

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market

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