Wind power forecasting using advanced neural networks models

Kariniotakis, Georges; Stavrakakis, G.; Nogaret, Eric

doi:10.1109/60.556376

Cited by 356 publications

(139 citation statements)

References 8 publications

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“…In the case of a large-scale wind power generation farm, e.g., one consisting of a large number m w of wind-turbines, the overall wind power output is estimated as the sum of the power output values sampled at different turbines for simplicity [21] …”

Section: Wind Power Modelmentioning

confidence: 99%

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Zhang

Wang

2011

Lecture Notes in Computer Science

137

111

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Abstract. To reduce the negative environmental implications (e.g., CO2 emission and global warming) caused by the rapidly increasing energy consumption, many Internet service operators have started taking various initiatives to operate their cloud-scale data centers with renewable energy. Unfortunately, due to the intermittent nature of renewable energy sources such as wind turbines and solar panels, currently renewable energy is often more expensive than brown energy that is produced with conventional fossil-based fuel. As a result, utilizing renewable energy may impose a considerable pressure on the sometimes stringent operation budgets of Internet service operators. Therefore, two key questions faced by many cloud-service operators are 1) how to dynamically distribute service requests among data centers in different geographical locations, based on the local weather conditions, to maximize the use of renewable energy, and 2) how to do that within their allowed operation budgets.In this paper, we propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP). We evaluate GreenWare with realworld weather, electricity price, and workload traces. Our experimental results show that GreenWare can significantly increase the use of renewable energy in cloud-scale data centers without violating the desired cost budget, despite the intermittent supplies of renewable energy in different locations and time-varying electricity prices and workloads.

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Section: Wind Power Modelmentioning

confidence: 99%

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Zhang

Wang

2011

Lecture Notes in Computer Science

137

111

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“…When basing predictions (largely) on wind speed data, several attempts have been made to perform very short-term wind energy forecasting using "intelligent" techniques such as Neural Networks (Kariniotakis et al 1996, Li et al 2001, Ricalde et al 2011, Shi et al 2011. Ricalde et al used Neural Networks for wind speed forecasting and compared between different networks (Ricalde et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Influence of neural network training parameters on short-term wind forecasting

Brka

Al-Abdeli

Kothapalli

2014

International Journal of Sustainable Energy

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This paper investigates factors which can affect the accuracy of short-term wind speed prediction when done over long periods spanning different seasons. Two types of Neural Networks are used to forecast power generated via specific horizontal axis wind turbines.Meteorological data used is for a specific Western Australian location.Results reveal that seasonal variations affect the prediction accuracy of the wind resource, but the magnitude of this influence strongly depends on the details of the Neural Network deployed. Factors investigated include the span of the time series needed to initially train the networks, the temporal resolution of this data, the length of training pattern within the overall span which are used to implement the predictions and whether the inclusion of solar irradiance data can appreciably affect wind speed prediction accuracy. There appears to be a relatively complex relationship between these factors and the accuracy of wind speed prediction via Neural Networks. Predicting wind speed based on Neural Networks trained using wind speed and solar irradiance data also increases the prediction accuracy of wind power generated, as can the type of network selected.

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“…ANN and GP is an appropriate method for wind power prediction and has been widely used (Alexiadis, Dokopoulos, Sahsamanoglou, & Manousaridis, 1998;Fan, Wang, Liu, & DAI, 2008;Kariniotakis, Stavrakakis, & Nogaret, 1996;Mohandes, Rehman, & Halawani, 1998;Seo & Hyeon, 2015) in renewable energy prediction. In order to form the ML model, monthly average values of metrological parameters for 85 locations in Queensland were extracted from the surface meteorology and solar energy release 6.0 (SSE 6.0) (Stackhouse & Whitlock, 2009).…”

Section: Introductionmentioning

confidence: 99%