17,244 research outputs found
Predicting the energy output of wind farms based on weather data: important variables and their correlation
Pre-print available at: http://arxiv.org/abs/1109.1922Wind energy plays an increasing role in the supply of energy world wide. The energy output of a wind farm is highly dependent on the weather conditions present at its site. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproduction. In this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters, we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We report on the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly supplied weather data. © 2012 Elsevier Ltd.Ekaterina Vladislavleva, Tobias Friedrich, Frank Neumann, Markus Wagne
Approaches for multi-step density forecasts with application to aggregated wind power
The generation of multi-step density forecasts for non-Gaussian data mostly
relies on Monte Carlo simulations which are computationally intensive. Using
aggregated wind power in Ireland, we study two approaches of multi-step density
forecasts which can be obtained from simple iterations so that intensive
computations are avoided. In the first approach, we apply a logistic
transformation to normalize the data approximately and describe the transformed
data using ARIMA--GARCH models so that multi-step forecasts can be iterated
easily. In the second approach, we describe the forecast densities by truncated
normal distributions which are governed by two parameters, namely, the
conditional mean and conditional variance. We apply exponential smoothing
methods to forecast the two parameters simultaneously. Since the underlying
model of exponential smoothing is Gaussian, we are able to obtain multi-step
forecasts of the parameters by simple iterations and thus generate forecast
densities as truncated normal distributions. We generate forecasts for wind
power from 15 minutes to 24 hours ahead. Results show that the first approach
generates superior forecasts and slightly outperforms the second approach under
various proper scores. Nevertheless, the second approach is computationally
more efficient and gives more robust results under different lengths of
training data. It also provides an attractive alternative approach since one is
allowed to choose a particular parametric density for the forecasts, and is
valuable when there are no obvious transformations to normalize the data.Comment: Corrected version includes updated equation (18). Published in at
http://dx.doi.org/10.1214/09-AOAS320 the Annals of Applied Statistics
(http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting
In weather forecasting, nonhomogeneous regression is used to statistically
postprocess forecast ensembles in order to obtain calibrated predictive
distributions. For wind speed forecasts, the regression model is given by a
truncated normal distribution where location and spread are derived from the
ensemble. This paper proposes two alternative approaches which utilize the
generalized extreme value (GEV) distribution. A direct alternative to the
truncated normal regression is to apply a predictive distribution from the GEV
family, while a regime switching approach based on the median of the forecast
ensemble incorporates both distributions. In a case study on daily maximum wind
speed over Germany with the forecast ensemble from the European Centre for
Medium-Range Weather Forecasts, all three approaches provide calibrated and
sharp predictive distributions with the regime switching approach showing the
highest skill in the upper tail
Feature-driven improvement of renewable energy forecasting and trading
M. A. Muñoz, J. M. Morales, and S. Pineda, Feature-driven Improvement of Renewable Energy Forecasting and Trading, IEEE Transactions on Power Systems, 2020.Inspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enhance both the quality of renewable energy forecasts and the competitive edge of renewable energy producers in electricity markets with a dual-price settlement of imbalances. The quality and economic gains brought by the proposed procedure essentially stem from the utilization of valuable predictors (also known as features) in a data-driven newsvendor model that renders a computationally inexpensive linear program. We illustrate the proposed procedure and numerically assess its benefits on a realistic case study that considers the aggregate wind power production in the Danish DK1 bidding zone as the variable to be predicted and traded. Within this context, our procedure leverages, among others, spatial information in the form of wind power forecasts issued by transmission system operators (TSO) in surrounding bidding zones and publicly available in online platforms. We show that our method is able to improve the quality of the wind power forecast issued by the Danish TSO by several percentage points (when measured in terms of the mean absolute or the root mean square error) and to significantly reduce the balancing costs incurred by the wind power producer.European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (grant agreement No. 755705)
Spanish Ministry of Economy, Industry, and Competitiveness through project ENE2017-83775-P
Benefits of spatio-temporal modelling for short term wind power forecasting at both individual and aggregated levels
The share of wind energy in total installed power capacity has grown rapidly
in recent years around the world. Producing accurate and reliable forecasts of
wind power production, together with a quantification of the uncertainty, is
essential to optimally integrate wind energy into power systems. We build
spatio-temporal models for wind power generation and obtain full probabilistic
forecasts from 15 minutes to 5 hours ahead. Detailed analysis of the forecast
performances on the individual wind farms and aggregated wind power are
provided. We show that it is possible to improve the results of forecasting
aggregated wind power by utilizing spatio-temporal correlations among
individual wind farms. Furthermore, spatio-temporal models have the advantage
of being able to produce spatially out-of-sample forecasts. We evaluate the
predictions on a data set from wind farms in western Denmark and compare the
spatio-temporal model with an autoregressive model containing a common
autoregressive parameter for all wind farms, identifying the specific cases
when it is important to have a spatio-temporal model instead of a temporal one.
This case study demonstrates that it is possible to obtain fast and accurate
forecasts of wind power generation at wind farms where data is available, but
also at a larger portfolio including wind farms at new locations. The results
and the methodologies are relevant for wind power forecasts across the globe as
well as for spatial-temporal modelling in general
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