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Mutriku has recently become the first commercial wave farm to release its operating data. The plant has 14 OWC operating turbines, and this study has conducted an analysis of hourly data corresponding to the 2014–2016 period. The plant's capacity factor has been calculated for this period, and its seasonal evolution characterized. Additionally, a plant efficiency index has been defined as the ratio between the wave energy flux at a reference buoy and the average power generation across the active turbines. The Mutriku wave farm's annual output in the period analysed has been 246,468.7 kW-h, with an average of around ten working turbines. The results indicate that Mutriku's average capacity factor is around 0.11, with higher values in winter than in summer. These values are below the capacity factors reported for other renewable energy sources. The plant efficiency index is 0.26, and further advances in regulation and control may also raise this parameter's values, as may lower rated power alternators. This will also help to improve the Mutriku wave plant's capacity factor, and OWC technology in general. [ABSTRACT FROM AUTHOR]

In 2008, the first commercial wave farm came online in Portugal. As with other types of renewable energy, the electricity obtained from waves has the drawback of intermittency. Knowing a few hours ahead how much energy waves will hold can contribute to a better management of the electricity grid. In this work, three types of statistical models have been used to create up to 24-h forecasts of the zonal and meridional components of wave energy flux (WEF) levels at three directional buoys located off the coast in the Bay of Biscay. Each model’s performance has been compared at a 95% confidence level with the simplest prediction (persistence of levels), along with the forecasts provided by the physics-based WAve Modeling (WAM) wave model at the nearest grid point. The results indicate that for forecasting horizons between 3 and roughly 16 h ahead, the statistical models built on random forests (RFs) outperform the rest, including WAM and persistence. [ABSTRACT FROM PUBLISHER]

This paper analyzes the performance of three types of statistical models and a well-known physics-based model for forecasting the wave energy flux. The forecasts are run over horizons of 1–24 h at five buoys located in the Bay of Biscay. The data resolution is hourly. The 1999–2005 timeframe is used to train the models. The forecasts are run and evaluated over the six-year period from 2006 to 2012. The statistical forecasting models use three techniques: analogues, random forests (a machine learning algorithm) and a combination of the two. The physics model is the Wave Model (WAM). The forecasts are compared at a 95% confidence level with the simplest prediction – Persistence – and also with the nearest grid point of the WAM forecasts. Over horizons between 3 and 16–19 h at locations near the coast (where wave farms may be installed), the random forests models outperform the others, including WAM and Persistence. These models exploit the inherent predictability associated with the strong autocorrelation present in ocean energy values. The additional prognostic capabilities that random forests models provide over Persistence, are due to their ability to sucessfully incorporate the information that both, atmospheric and sea-state variables provide. [ABSTRACT FROM AUTHOR]

Abstract: In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network''s capability to correctly follow and represent SO2 fields in Bilbao, in the frame of a continuous network optimization process. The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area. To assess the spatial variability of SO2 measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R. [Copyright &y& Elsevier]