Probabilistic forecasting of the wave energy flux

Reikard, Gordon; Bidlot, Jean‐Raymond

doi:10.1016/j.apenergy.2011.12.040

Cited by 88 publications

(30 citation statements)

References 20 publications

(25 reference statements)

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“…In particular, it has been extensively studied in renewable energy forecasting and, owing to its parsimony, it has turned out to be a very tough competitor to beat. Applications include, among others, forecasting of wind power generation [34], online power forecasting [35] and wave energy flux [36].…”

Section: Intra-day Solar Forecasts With a Linear Recursive Model (Armmentioning

confidence: 99%

Solar Forecasting in a Challenging Insular Context

Lauret¹,

Lorenz

David³

2016

Atmosphere

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This paper aims at assessing the accuracy of different solar forecasting methods in the case of an insular context. Two sites of La Réunion Island, Le Tampon and Saint-Pierre, are chosen to do the benchmarking exercise. Réunion Island is a tropical island with a complex orography where cloud processes are mainly governed by local dynamics. As a consequence, Réunion Island exhibits numerous micro-climates. The two aforementioned sites are quite representative of the challenging character of solar forecasting in the case of a tropical island with complex orography. Hence, although distant from only 10 km, these two sites exhibit very different sky conditions. This work focuses on day-ahead and intra-day solar forecasting. Day-ahead solar forecasts are provided by the European Center for Medium-Range Weather Forecast (ECMWF). This organization maintains and runs the Numerical Weather Prediction (NWP) model named Integrated Forecast System (IFS). In this work, post-processing techniques are applied to refine the output of the IFS model for day-ahead forecasting. Statistical models like a recursive linear model or a nonlinear model such as an artificial neural network are used to produce the intra-day solar forecasts. It is shown that a combination of the IFS model and the neural network model further improves the accuracy of the forecasts.

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Section: Intra-day Solar Forecasts With a Linear Recursive Model (Armmentioning

confidence: 99%

Solar Forecasting in a Challenging Insular Context

Lauret¹,

Lorenz

David³

2016

Atmosphere

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“…As with other forms of renewable energy, wave energy can be highly variable and of limited predictability. However, wave power flux can be well probabilistically forecasted 48 h in advance [6]. The planning and operation of wave energy converters require reliable estimates of the available power and their seasonal variations and [7] provide a thorough resource assessment for the Black Sea based on monthly, seasonal, and annual distributions of wave height and wave power.…”

Section: Introductionmentioning

confidence: 99%

Direct AC–AC grid interface converter for ocean wave energy system

Tsang

Chan

2015

Energy Conversion and Management

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“…The proposed multi-model ensemble method is compared to both probabilistic base predictors and also to a benchmark based on the probabilistic persistence method (PPM) [41], in order to verify its usefulness. The comparison is performed numerically in terms of PLF and graphically through the inspection of PIT histograms [42] and reliability diagrams [43].…”

Section: Numerical Applicationsmentioning

confidence: 99%

A New Ensemble Probabilistic Method for Short-Term Photovoltaic Power Forecasting

Bracale¹,

Carpinelli²,

Falco³

2016

Sustainable Energy - Technological Issues, Applications and Case Studies

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The high penetration of photovoltaic (PV) systems led to their growing impact on the planning and operation of actual distribution systems. However, the uncertainties due to the intermittent nature of solar energy complicate these tasks. Therefore, high-quality methods for forecasting the PV power are now essential, and many tools have been developed in order to provide useful and consistent forecasts. This chapter deals with probabilistic forecasting methods of PV system power, since they have recently drawn the attention of researchers as appropriate tools to cope with the unavoidable uncertainties of solar source. A new multi-model probabilistic ensemble is proposed; it properly combines a Bayesian-based and a quantile regression-based probabilistic method as individual predictors. Numerical applications based on actual irradiance data give evidence of the probabilistic performances of the proposed method in terms of both sharpness and calibration.

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Probabilistic forecasting of the wave energy flux

Cited by 88 publications

References 20 publications

Solar Forecasting in a Challenging Insular Context

Solar Forecasting in a Challenging Insular Context

Direct AC–AC grid interface converter for ocean wave energy system

A New Ensemble Probabilistic Method for Short-Term Photovoltaic Power Forecasting

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