This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 hours. The data used is fifteen-minute observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to two hours ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35 % is achieved by the ARX model compared to a proposed reference model.

Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time-dependent and multi-stage decision-making problems e.g. optimal operation of combined wind-storage systems or multiple-market trading with different gate closures. This issue is addressed here by describing a method that permits the generation of statistical scenarios of short-term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion of series of prediction errors to a multivariate Gaussian random variable, the interdependence structure of which can then be summarized by a unique covariance matrix. Such matrix is recursively estimated in order to accommodate long-term variations in the prediction error characteristics. The quality and interest of the methodology are demonstrated with an application to the test case of a multi-MW wind farm over a period of more than two years.

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Predictions of wind power production for horizons up to 48–72 h ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the conditional expectation of the wind generation for each look‐ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from non‐parametric methods, and then take the form of a single or a set of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point predictions of wind power. These distributions are defined by a number of quantile forecasts with nominal proportions spanning the unit interval. The relevance and interest of the introduced evaluation framework are discussed. Copyright © 2007 John Wiley & Sons, Ltd.

Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective evaluation of model performance.This paper proposes a standardized protocol for the evaluation of short-term windpower prediction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated, using results from both on-shore and offshore wind farms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems. NOMENCLATURE P inst , W ind farm installed capacity (in kW or MW) k = 1, 2, . . . , k max , Prediction time-step (also called lead time or look-ahead time) k max , M aximum prediction horizon N,Number of data used for the model evaluation P(t), Measured power at time t (in kW or MW), which usually corresponds to the average power over the previous time period P (t + k|t), Power forecast for time t + k made at time origin t (in kW or MW) e(t + k|t), Error corresponding to time t + k for the prediction made at time origin t (in kW or MW) ε(t + k|t),

For operational planning it is important to provide information about the situation‐dependent uncertainty of a wind power forecast. Factors which influence the uncertainty of a wind power forecast include the predictability of the actual meteorological situation, the level of the predicted wind speed (due to the non‐linearity of the power curve) and the forecast horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables are considered, some inspired by the literature. The article contains an overview of related work within the field. An existing wind power forecasting system (Zephyr/WPPT) is considered and it is shown how analysis of the forecast error can be used to build a model of the quantiles of the forecast error. Only explanatory variables or indices which are predictable are considered, whereby the model obtained can be used for providing situation‐dependent information regarding the uncertainty. Finally, the article contains directions enabling the reader to replicate the methods and thereby extend other forecast systems with situation‐dependent information on uncertainty. Copyright © 2005 John Wiley & Sons, Ltd.

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The use of volatile fatty acids (VFA) as process indicators in biogas reactors treating manure together with industrial waste was studied. At a full-scale biogas plant, an online VFA sensor was installed in order to study VFA dynamics during stable and unstable operation. During stable operation acetate increased significantly during the feeding periods from a level of 2-4 to 12-17 mM, but the concentration generally dropped to about the same level as before feeding. The fluctuations in the propionate were more moderate than for acetate but the average level rose during 1 week of operation from 0.6 to 2.9 mM. A process disturbance caused by overloading with industrial waste was reflected by a significant increase in all VFA concentrations. During the recovery of the process, the return of propionate back to the steady-state level was 2-3 days slower than any other VFA and propionate could best describe the normalizing of the process. In a lab-scale continuously stirred tank reactor experiment, with manure as main substrate, the prospective of using either propionate concentration or methane production as single process indicators was studied. Propionate was found to be the best indicator. Thus, a process breakdown caused by organic overloading with meat and bone meal and lipids was indicated by changes in propionate concentration 12-18 days before a decrease in methane production was observed. Furthermore, a more efficient and stable utilization of the substrate was observed when propionate was used as process indicator.