GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach

Nagy, Gábor P.; Barta, Gergo; Kazi, Sandor; Borbély, Gyula; Simon, Gábor

doi:10.1016/j.ijforecast.2015.11.013

Cited by 95 publications

(37 citation statements)

References 8 publications

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“…In [4] it is indicated that a usual feature of renewable energy is that the output of power plants largely depends on weather conditions. Thus, the integration of knowledge or data about weather could benefit the time series modelling process to provide an accurate forecast instead of a unique approach with one single value corresponding to the consumption data [17,33].…”

Section: Narx Modelmentioning

confidence: 99%

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An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs (NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

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Section: Narx Modelmentioning

confidence: 99%

“…Therefore, there are a multitude of models and methodologies related to this field, applied to energy efficiency. Nagy et al [4] suggest a generalized additive tree ensemble approach to predict solar and wind power generation. Yuan et al [5] use autoregressive moving average model (ARIMA), grey model GM (1,1), and a hybrid…”

Section: Introductionmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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“…In data science, our most recent research is about the renewable energy forecast by using distributed and privacy preserved machine-learning methods [180,181]. At end of the year, a successful R&D project was finished with OTP Bank, in which a new interpretable classification method was created and implemented to increase the efficiency of prediction.…”

Section: Data Science and Content Technologiesmentioning

confidence: 99%

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2016

Charaf

Harsányi

Poppe

et al. 2017

Period. Polytech. Elec. Eng. Comp. Sci.

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Preface Since being established in 1949, the Faculty of Electrical Engineering and Informatics (VIK) BME has played a flagship role in the development of electronics, IT and computer science in Hungary.We László Jakab (dean, BME VIK) János Levendovszky (vice-dean in charge of scientific affairs, BME VIK) are proud of combining engineering applications with sound scientific results, which is the assurance of high-level industrial collaboration leading to novel results and innovation. The various collaborations with our industrial partners has made it clear that the industry expects methods and results which make their industrial processes more effective and increase productivity and quality. Thus, participation in these collaborations give a competitive edge and ensure the continuous development of VIK. These factors have positioned our Department of Automation and Applied InformaticsDepartment of Automation and Applied Informatics (AUT) is one of the largest and dynamically developing departments at BME with diverse scope competences such as control theory, embedded systems, software modelling, applied software development, Internet of Things, power electronics, mechatronics, and many others. The department's main activities are education, research and development. Training versatile electrical and software engineers with solid practical knowledge is our top priority. Our profile also includes developing high quality software and hardware solutions for industry partners. All of our activities are backed by strong research background.In 2016, the department organized and hosted a number of domestic and international events. In cooperation with the industry, AUT has organized the IoT4U conference. It was dedicated to Internet of Things, cloud and data management as well as the integration of IoT into various formats. RobonAUT, the yearly domestic amateur robotics contest was organized by the department in February 2016. The yearly contest is open to teams of students, organized either in student projects within the department or independent research teams.The next sections summarize the key research results of the department in year 2016. Software Development Methods and Mobile PlatformsThe diversity of mobile platforms necessitates the development of the same mobile application for all major mobile platforms, what requires considerable development effort. Mobile application developers are multiplatform developers, but they still prioritize the platforms, therefore, not all platforms are equally important for them. Appropriate methods, processes and tools are required to support the development in order to achieve better productivity.The research on educational mobile applications that adapt to the mental state of the user required a method for estimating the difficulty of tasks found in the serious games. There are generated or random assignments where the effort required from the user is not known or cannot be estimated in advance.61 (2) IoT and Smart CityThe Internet of Things (IoT) is transforming the surroun...

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“…Gaussian Process Regression (GPR) [14] and Markov Chain (MC) [15] models are also becoming more frequent in the literature. The global forecasting competition GEFCOM 2014 [16] showed that the most efficient algorithms were often non-parametric, such as Quantile Regression Forests (QRF) [17] and Gradient Boosting (GB) [18]. However, [19] performed a comparison of several nonparametric models and found that the performance difference between the tested models was low.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Seamless Probabilistic Photovoltaic Power Forecasting Covering Multiple Time Frames

Carrière

Vernay²,

Pitaval

et al. 2020

IEEE Trans. Smart Grid

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GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach

Cited by 95 publications

References 8 publications

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2016

A Novel Approach for Seamless Probabilistic Photovoltaic Power Forecasting Covering Multiple Time Frames

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