Forecasting Electricity Consumption with Neural Networks and Support Vector Regression

Oğcu, Gamze; Demirel, Omer F.; Zaim, Selim

doi:10.1016/j.sbspro.2012.09.1144

Cited by 100 publications

(39 citation statements)

References 16 publications

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“…The network forecast generated a near point-to-point match with the actual data collected for the years [2005][2006][2007][2008]. In a similar vein, ANN predictions were 95% accurate in the forecasting of electrical usage in Turkey (Oğcu, Demirel & Zaim, 2012). Clearly, forecasting energy availability and consumption will be a critical issue as countries face diminishing supplies and attempt to regulate the effect on the global climate and energy consumption.…”

Section: Articulationmentioning

confidence: 83%

Neural Network and Multivariate Analyses: Pattern Recognition in Academic and Social Research

et al. 2013

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Neural networks are the modern tools that focus most heavily on the logical structure of measurement/assessment, as well as the actual results we attempt to identify by way of scientific inquiry. Employing the Self-Organizing Map (SOM) neural network, we reexamined a well-recognized and commonly employed dataset from a popular applied multivariate statistics text by Stevens (2009). Using this textbook dataset as an exemplar, we provide a preliminary guide to neural networking approaches to the analysis of behavioral outcomes. When employing conventional multivariate procedures only, the sample dataset demonstrated significant familywise error rates; however, these outcomes did not provide sufficient information for identifying the curvilinear patterns that existed within these records. When converted to natural logs and reanalyzed by the SOM, the exemplar dataset showed the actual best fit performance patterns exhibited by all members of the experimental and control groups. The SOM and related neural network algorithms appear to have unique potential in the recognition of nonlinear but unified data patterns frequently exhibited within academic and social outcomes. In particular, the SOM allows the researcher to conduct a "finer grain" analysis identifying critically important similarities and differences that can inform treatment well beyond the probability values derived from conventional statistical techniques.

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Section: Articulationmentioning

confidence: 83%

Neural Network and Multivariate Analyses: Pattern Recognition in Academic and Social Research

et al. 2013

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“…Support Vector Machines (SVM) have been applied successfully to many different real-world problems like electricity load [42] and consumption forecasting [43] and is based on the statistical learning theory [44]. Basically, the SVM maps nonlinearly the original data into a higher dimensional feature space using a kernel (e.g., the Gaussian Radial Basis Function) before solving the machine learning task as a convex optimization problem.…”

Section: Kernel Quantile Regression (Kqr)mentioning

confidence: 99%

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

2019

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Reliable predictions of the energy consumption and production is important information for the management and integration of renewable energy sources. Several different Machine Learning (ML) methodologies have been tested for predicting the energy consumption/production based on the information of hydro-meteorological data. The methods analysed include Multivariate Adaptive Regression Splines (MARS) and various Quantile Regression (QR) models like Quantile Random Forest (QRF) and Gradient Boosting Machines (GBM). Additionally, a Nonhomogeneous Gaussian Regression (NGR) approach has been tested for combining and calibrating monthly ML based forecasts driven by ensemble weather forecasts. The novelty and main focus of this study is the comparison of the capability of ML methods for producing reliable predictive uncertainties and the application of monthly weather forecasts. Different skill scores have been used to verify the predictions and their uncertainties and first results for combining the ML methods applying the NGR approach and coupling the predictions with monthly ensemble weather forecasts are shown for the southern Switzerland (Canton of Ticino). These results highlight the possibilities of improvements using ML methods and the importance of optimally combining different ML methods for achieving more accurate estimates of future energy consumptions and productions with sharper prediction uncertainty estimates (i.e., narrower prediction intervals).

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“…A. Azadeh and et al, analyzed and forecasting electricity consumption in industrial sectors by using neural network model [4]. Gamze Ogcu and et al, had published forecasting electricity consumption with neural networks and support vector regression [5]. Eva GonzalezRomera and et al investigated forecasting of the electric energy demand trend and monthly fluctuation with neural networks [6].…”

Section: Related Workmentioning

confidence: 99%

Analysis and Forecasting of Electric Power Energy Consumption in IoT Environments

Ragu¹,

Yang²,

Chae³

et al. 2018

IJGDC

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Forecasting Electricity Consumption with Neural Networks and Support Vector Regression

Cited by 100 publications

References 16 publications

Neural Network and Multivariate Analyses: Pattern Recognition in Academic and Social Research

Neural Network and Multivariate Analyses: Pattern Recognition in Academic and Social Research

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

Analysis and Forecasting of Electric Power Energy Consumption in IoT Environments

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