Now showing 1 - 8 of 8
  • Publication
    Adaptive post-processing of short-term wind forecasts for energy applications
    (Wiley, 2011-04) ;
    We present a new method of reducing the error in predicted wind speed, thus enabling better management of wind energy facilities. A numerical weather prediction model, COSMO, was used to produce 48 h forecast data every day in 2008 at horizontal resolutions of 10 and 3 km. A new adaptive statistical method was applied to the model output to improve the forecast skill. The method applied corrective weights to a set of forecasts generated using several post-processing methods. The weights were calculated based on the recent skill of the different forecasts. The resulting forecast data were compared with observed data, and skill scores were calculated to allow comparison between different post-processing methods. The total root mean square error performance of the composite forecast is superior to that of any of the individual methods.
      613Scopus© Citations 13
  • Publication
    Wind/PV: errors, correlations & forecasts
    There is an increasing interest in using PV to generate electricity as the cost of PV modules decreases. This study will present results on forecast skill, systematic errors and correlations of NWP data for wind and PV. Thirty years of radiation data from the MERRA2 and ERA-Interim reanalyses were compared with hourly data from a representative selection of Irish meteorological service synoptic weather stations. Standard skill scores were calculated for relevant parameters: 10-metre wind speed and direction, incoming shortwave radiation, 2-metre temperature. Situations where the reanalysis radiation skill scores were poor were investigated by running WRF forecasts on a domain centred over Ireland. Forecasts were initialised at 12UTC and run for forecast time horizons from one to five days. Time series analysis allowed the correlation between skill scores for wind and PV parameters to be investigated. Finally, a few popular post-processing techniques were applied to investigate the potential to remove systematic errors.
      69
  • Publication
    Forecasting for an Integrated Energy System
    (ESIG, 2018-06-20)
    Outline: 1. Forecast Applications 2. Forecast Ranges 3. Reanalysis Data 4. ShortWave Radiation 5. Wind/SW correlations 6. Systematic Errors 7. PostProcessing 8. Weather and Demand 9. Probabilistic Forecasting 10. MultiVariate Spatial PP
      77
  • Publication
    Reducing errors of wind speed forecasts by an optimal combination of post-processing methods
    (Wiley-Blackwell, 2011-09-13) ; ;
    Seven adaptive approaches to post-processing wind speed forecasts are discussed and compared. 48-hour forecasts are run at horizontal resolutions of 7 km and 3 km for a domain centred over Ireland. Forecast wind speeds over a two year period are compared to observed wind speeds at seven synoptic stations around Ireland and skill scores calculated. Two automatic methods for combining forecast streams are applied. The forecasts produced by the combined methods give bias and root mean squared errors that are better than the numerical weather prediction forecasts at all station locations. One of the combined forecast methods results in skill scores that are equal to or better than all of its component forecast streams. This method is straightforward to apply and should prove beneficial in operational wind forecasting.
      832Scopus© Citations 51
  • Publication
    A high-resolution, multi-model analysis of Irish temperatures for the mid-21st century
    There is a paucity of dynamically downscaled climate model output at a high resolution over Ireland, of temperature projections for the mid-21st century. This study aims to address this shortcoming. A preliminary investigation of global climate model (GCM) data and high-resolution regional climate model (RCM) data shows that the latter exhibits greater variability over Ireland by reducing the dominance of the surrounding seas on the climate signal. This motivates the subsequent dynamical downscaling and analysis of the temperature output from three high-resolution (4–7 km grid size) RCMs over Ireland. The three RCMs, driven by four GCMs from CMIP3 and CMIP5, were run under different Special Report on Emissions Scenarios (SRES) and representative concentration pathway (RCP) future scenarios. Projections of mean and extreme temperature changes are considered for the mid-century (2041–2060) and assessed relative to the control period of 1981–2000. Analysis of the RCM data shows that annual mean temperatures are projected to rise between 0.4 and 1.8 °C above control levels by mid-century. On a seasonal basis, results differ by forcing scenario. Future summers have the largest projected warming under RCP 8.5, where the greatest warming is seen in the southeast of Ireland. The remaining two high emission scenarios (SRESs A1B and A2) project future winters to have the greatest warming, with almost uniform increases of 1.5–2 °C across the island. Changes in the bidecadal 5th and 95th percentile values of daily minimum and maximum temperatures, respectively, are also analysed. The greatest change in daily minimum temperature is projected for future winters (indicating fewer cold nights and frost days), a pattern that is consistent across all scenarios/forcings. An investigation into the distribution of temperature under RCP 8.5 shows a strong summer increase compounded by increased variability, and a winter increase compounded by an increase in skewness.
      442Scopus© Citations 9
  • Publication
    A 34-year simulation of wind generation potential for Ireland and the impact of large-scale atmospheric pressure patterns
    To study climate-related aspects of power system operation with large volumes of wind generation, data with sufficiently wide temporal and spatial scope are required. The relative youth of the wind industry means that long-term data from real systems are not available. Here, a detailed aggregated wind power generation model is developed for the Republic of Ireland using MERRA reanalysis wind speed data and verified against measured wind production data for the period 2001–2014. The model is most successful in representing aggregate power output in the middle years of this period, after the total installed capacity had reached around 500 MW. Variability on scales of greater than 6 h is captured well by the model; one additional higher resolution wind dataset was found to improve the representation of higher frequency variability. Finally, the model is used to hindcast hypothetical aggregate wind production over the 34-year period 1980–2013, based on existing installed wind capacity. A relationship is found between several of the production characteristics, including capacity factor, ramping and persistence, and two large-scale atmospheric patterns – the North Atlantic Oscillation and the East Atlantic Pattern.
      347Scopus© Citations 22
  • Publication
    The future of forecasting for renewable energy
    Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state-of-the-art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure.
      1284Scopus© Citations 69
  • Publication
    Spatial Bayesian hierarchical modelling of extreme sea states
    A Bayesian hierarchical framework is used to model extreme sea states, incorporating a latent spatial process to more effectively capture the spatial variation of the extremes. The model is applied to a 34-year hindcast of significant wave height off the west coast of Ireland. The generalised Pareto distribution is fitted to declustered peaks over a threshold given by the 99.8th percentile of the data. Return levels of significant wave height are computed and compared against those from a model based on the commonly-used maximum likelihood inference method. The Bayesian spatial model produces smoother maps of return levels. Furthermore, this approach greatly reduces the uncertainty in the estimates, thus providing information on extremes which is more useful for practical applications.
      270Scopus© Citations 7