Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Bandara, Kasun"'
Regional solar power forecasting, which involves predicting the total power generation from all rooftop photovoltaic systems in a region holds significant importance for various stakeholders in the energy sector. However, the vast amount of solar pow
Externí odkaz:
http://arxiv.org/abs/2403.01653
Autor:
Pekaslan, Direnc, Alonso-Moral, Jose Maria, Bandara, Kasun, Bergmeir, Christoph, Bernabe-Moreno, Juan, Eigenmann, Robert, Einecke, Nils, Ergen, Selvi, Godahewa, Rakshitha, Hewamalage, Hansika, Lago, Jesus, Limmer, Steffen, Rebhan, Sven, Rabinovich, Boris, Rajapasksha, Dilini, Song, Heda, Wagner, Christian, Wu, Wenlong, Magdalena, Luis, Triguero, Isaac
This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key competitions: the IEEE Computational Intelligence Society (IEEE-CIS) Tec
Externí odkaz:
http://arxiv.org/abs/2311.04007
Machine learning (ML) based time series forecasting models often require and assume certain degrees of stationarity in the data when producing forecasts. However, in many real-world situations, the data distributions are not stationary and they can c
Externí odkaz:
http://arxiv.org/abs/2304.01512
We apply classical statistical methods in conjunction with the state-of-the-art machine learning techniques to develop a hybrid interpretable model to analyse 454,897 online customers' behavior for a particular product category at the largest online
Externí odkaz:
http://arxiv.org/abs/2212.02255
Publikováno v:
Expert Systems with Applications 205 (2022)
Distributed, small-scale solar photovoltaic (PV) systems are being installed at a rapidly increasing rate. This can cause major impacts on distribution networks and energy markets. As a result, there is a significant need for improved forecasting of
Externí odkaz:
http://arxiv.org/abs/2206.10795
The decomposition of time series into components is an important task that helps to understand time series and can enable better forecasting. Nowadays, with high sampling rates leading to high-frequency data (such as daily, hourly, or minutely data),
Externí odkaz:
http://arxiv.org/abs/2107.13462
Autor:
Bandara, Kasun A., Politis, Sebastian N., Sganga, Daniela Eliana, Sørensen, Sune Riis, Koumpiadis, Paraskevas, Tomkiewicz, Jonna, Vadstein, Olav
Publikováno v:
In Aquaculture 15 May 2024 585
Publikováno v:
Knowledge-Based Systems (2021) 107518
With large quantities of data typically available nowadays, forecasting models that are trained across sets of time series, known as Global Forecasting Models (GFM), are regularly outperforming traditional univariate forecasting models that work on i
Externí odkaz:
http://arxiv.org/abs/2012.15059
In the current context of Big Data, the nature of many forecasting problems has changed from predicting isolated time series to predicting many time series from similar sources. This has opened up the opportunity to develop competitive global forecas
Externí odkaz:
http://arxiv.org/abs/2012.12485
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univaria
Externí odkaz:
http://arxiv.org/abs/2008.02663