Using conditional kernel density estimation for wind power density forecasting

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate VARMA-GARCH (vector autoregressive moving average-generalized autoregressive conditional heteroscedastic) model, with a Student t distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms

Atmospheric turbulence profiling with SLODAR using multiple adaptive optics wavefront sensors

The slope detection and ranging (SLODAR) method recovers atmospheric turbulence profiles from time averaged spatial cross correlations of wavefront slopes measured by Shack-Hartmann wavefront sensors. The Palomar multiple guide star unit (MGSU) was set up to test tomographic multiple guide star adaptive optics and provided an ideal test bed for SLODAR turbulence altitude profiling. We present the data reduction methods and SLODAR results from MGSU observations made in 2006. Wind profiling is also performed using delayed wavefront cross correlations along with SLODAR analysis. The wind profiling analysis is shown to improve the height resolution of the SLODAR method and in addition gives the wind velocities of the turbulent layers

Assessing the representativeness of wind data for wind turbine site evaluation

Once potential wind turbine sites (either for single installations or clusters) are identified through siting procedures, actual evaluation of the sites must commence. This evaluation is needed to obtain estimates of wind turbine performance and to identify hazards to the machine from the turbulence component of the atmosphere. These estimates allow for more detailed project planning and for preliminary financing arrangements to be secured. The site evaluation process can occur in two stages: (1) utilizing existing nearby data, and (2) establishing and monitoring an onsite measurement program. Since step (2) requires a period of at least 1 yr or more from the time a potential site has been identified, step (1) is often an essential stage in the preliminary evaluation process. Both the methods that have been developed and the unknowns that still exist in assessing the representativeness of available data to a nearby wind turbine site are discussed. How the assessment of the representativeness of available data can be used to develop a more effective onsite meteorological measurement program is also discussed

A good practice guide on the sources and magnitude of uncertainty arising in the practical measurement of environmental noise

A brief introduction to measurement uncertainty, uncertainty budgets, and inter-comparison exercises (repeated measurements), is provided in Chapter 2. The procedure forformulating an uncertainty budget and evaluating magnitudes is outlined in greater detail in Chapter 3. A flow chart summarising this process, and a checklist for the identification of sources of measurement uncertainty are included at the end of the chapter. Two example measurement exercises with corresponding uncertainty budgets are presented in Chapter 4. Some of the more commonly encountered sources of measurement uncertainty are outlined in Chapter5. Where possible, information on magnitudes or pointers to where that information can be found are included. The more important sources of uncertainty are highlighted, and “good practice guidelines” provided to help the practitioner identify means of reducing their effect. Case studies illustrating some of the points made in Chapter 5,and listing of relevant guidelines and further reading are provided in the Appendices

Quantifying and reducing uncertainty in tidal energy yield assessments

Tidal stream energy has the potential to contribute to a diverse future energy mix. As the industry moves towards commercialisation and array scale deployment, there is an opportunity to better understand the uncertainties around energy yield assessments. Energy yield assessments are used widely in the wind industry to evaluate the potential energy production from a prospective project. One of the key challenges is to quantify and reduce uncertainty in energy yield assessment. This thesis investigates ways to achieve this through utilising lessons learnt from the established wind industry. An evaluation of both the wind and tidal energy yield assessment process is conducted, highlighting where synergies can be used to increase understanding of uncertainty for the nascent tidal industry. The processes are comparable starting with a campaign to collect site data to characterise the resource at the measurement location. The next stage is to evaluate the long term variations, however this is where the two methods differ. Analysis of long term wind effects requires correlations to be made between short term site data and long term reference data from alternative sources. An assessment of tidal variations over longer periods utilises harmonic analysis, which is capable of deconstructing the individual astronomical variations of the tide and reconstructing them to predict future variations. Despite harmonic analysis being able to determine the astronomical effects of the tide, there are uncertainties in the measurements of tidal flow which are associated with non-astronomical effects. Effects such as turbulence introduce uncertainty when evaluating measured tidal data. This is one area which is investigated further in the thesis. Methods to evaluate the turbulence intensity from real ADCP data are investigated. The next stages require creating a numerical model of the site to extrapolate the data spatially to other areas of interest (such as a turbine location). Energy yield predictions for both wind and tidal are made by combining a power curve with the long term resource. The energy yield outputs are then adjusted to account for energy losses and uncertainties are applied to produce final energy yield values with the attributed probability values associated. Statistical methods are applied to harmonic analysis to assess the level of uncertainty in long term predictions of tidal variations. A method using spectral analysis is applied to evaluate the residuals between measured and modelled data and proves to be accurate at determining missing tidal constituents from the analysis. A method for evaluating the turbulence intensity of the flow is shown, to better understand the stochastic nature of the tidal signal. An investigation is conducted to assess the propagation of bed friction uncertainty, in hydrodynamic modelling, and the resulting impact on the predicted power output from a theoretical fence of tidal turbines spanning a tidal channel. The methodology is based on first conducting sensitivity studies by varying a parameter in the model and calculating the power. Then using a mean and standard deviation for the input parameter, the impact of the uncertainty can be transferred to the estimate of power. The results show that a larger uncertainty associated with the bed roughness tends to over predict the estimation of power. This work aims to inform the standardisation of practices and guidelines in tidal resource assessment and to support developers, consultants and financiers in future tidal energy yield assessments. The final chapter includes procedural recommendations for future tidal energy projects, summarising methods to calculate uncertainty and recommendations to reduce them