wind power forecasting

A histogram of TARGETVAR in the training data set is shown in Figure 1. For some basic explanations of the workings of wind turbines, see the, The original data set contains all 32 wind velocity measurements made. Random Forest 2 and GAMLSS, the top two winners on the public leaderboard, don’t do so well on the private leaderboard, where XGBoost is the clear winner. These numbers can be compared with the top private leaderboard score obtained by the winner of the hackathon: RMSE=0.169463. This book addresses scientists and engineers working in wind energy related R and D and industry, as well as graduate students and nonspecialists researchers in the fields of atmospheric physics and meteorology. First a word about GAMLSS. The workshop covered topics . Forecasting wind power generation: a necessity. In that case I used ZONEID to split the data set into ten subsets, one per turbine, which I modeled independently. Note how wind velocities tend to be higher at 100m than at 10m. A possible solution is to use the so-called “beta inflated distribution”: a mixture of a beta distribution, a peak at 0, and a peak at 1. In practice, learners in gradient boosting models attempt to model the gradient of a loss function. An accurate forecast makes it possible for grid operators to schedule the economically efficient generation to meet the demand of electrical customers. We use cookies to help provide and enhance our service and tailor content and ads. Figure 4 illustrates this correlation for the turbine-1 power output. Unfortunately this does not generalize quite as nicely to the testing subset, which hints at overfitting. Hour 24 of one day is adjacent to hour 1 of the next day, just as day 365 of one year is adjacent to day 1 of the next year. Generalized linear models are very fast to train, but there may be a tradeoff to consider between training speed and modeling performance. Let’s start with the distribution of ​Y. Table 2: Features of the wind turbine data set. In contrast, a random forest averages the results of independent trees, and this must have a smoothing effect on singularities. 3. A Generalized Linear Model 6. The base year for the study is 2016, while forecasts have been provided from 2017 to 2025. Wind power forecasts still play a key role to address the challenges in electricity supply.operation Recently, several methods have been employed for the wind power forecasting. The uncertainty in wind power generation forces the power utilities to balance the power output of the generating units and bear the undesirable power balancing cost [9]. The global wind power market is expected to reach a cumulative installed capacity of 1,000 GW by the end of 2025, with Asia-Pacific as the dominant region. Random Forest Analysis 4. The aim was to predict the wind power that could be generated from the windmill for the next 15 days. This volume intends to bring out the original research work of researchers from academia and industry in understanding, quantifying and managing the risks associated with the uncertainty in wind variability in order to plan and operate a ... At several look ahead horizons, from (very) short-term up to 7-10 days ahead, with (sub-)hourly granularity, updated several times a day (subject to availability of data in near real time). Summary and Private Leaderboard 7. Forecasting App a. ANN Forecasting App b. ARIMA Forecasting APP c. WRF NETCDF Data Visualization and Extraction App 4. In other words, to predict turbine output at time t, use an average of wind speed measurements at times t, t-1, t-2, etc. Some The report reviews the individual central wind power forecasting systems in North America, both planned and operating, in more detail. The performance measure used to evaluate contributions was the root-mean-square error (RMSE), i.e. Turbines won’t produce any power if the wind speed falls below a threshold known as the cut-in speedFor some basic explanations of the workings of wind turbines, see the Windpower Program website. The conventional method discussed in this paper is the Autoregressive Moving Average (ARMA) which is one of the most robust and simple time-series methods. In the remainder of this post I describe the data set, feature engineering, and results obtained with a random forest and the gradient boosting algorithm known as XGBoost. However, a weighted average might make more sense, where older measurements are weighed down compared to recent ones. Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance ... In what follows it will be convenient to use an alternative parametrization of this distribution: Next, I linked two parameters of this distribution to linear combinations of the predictors. Figure 10 compares observed and predicted turbine output distributions in the training and testing subsets. Technical Note The wind power forecasting plays a vital role in renewable energy production. Wind power forecasts have been used operatively for over 20 years. training models. With our prediction systems Previento and Suncast, we deliver precise forecasts of the wind and solar power input for any on- and offshore sites worldwide as well as for control zones and grid node levels. , consider a regression model for example. 1. Figure 2 illustrates the effect of the cut-in speed for turbine 9. Google and its DeepMind AI subsidiary have combined weather data with power data from 700 megawatts of wind energy that Google sources in the Central US. This ensemble forecasting methodology also allows skillful prediction of forecast uncertainty. Suppose that the hackathon was still going on, and that you had to choose a model for the (hidden) private leaderboard. Authors: Harsh S. Dhiman, Dipankar Deb. Each data record contains the following fields: ⊕Table 2: Features of the wind turbine data set. Due to the dynamic and uncertain behavior of wind, it is really hard to catch the actual features of wind for accurate . Electricity markets in the United States are evolving. Results are shown for day-ahead wind power forecasts at the ISO level. 8.1mph, N. 4.7mphS San Francisco. This study, building on the extensive models developed for the Western Wind and Solar Integration Study (WWSIS), uses these WECC models to evaluate the operating cost impacts of improved day-ahead wind forecasts. Proper tuning of the hyperparameters of XGBoost requires some exploration, but is fairly straightforward. A brief overview and comparison of all these techniques is the main focus of the . I tried two methods for presenting the data to the model: Method 1 is to transform the data so that each record contains all 32 wind speed measurements made at a given timeThe original data set contains all 32 wind velocity measurements made every hour during the data-taking period. Wind power forecasting is a necessity in such markets in order to plan accurately and operate the energy system efficiently. More accurate wind generation forecasts could greatly reduce costs of wind integration services. Table 5: Root-mean-square errors on the public and private leaderboards for all models, including the final stacking model, which averages the outputs of Random Forest 2, XGBoost, and GAMLSS. The app allows wind farm operators to better capture margin price spread and reduce forecast-to-actual deviation penalties, while also allowing for optimized O&M planning. Forecast models ECMWF, GFS, NAM and NEMS Take a look at the private leaderboard: ⊕Table 4: Root-mean-square errors on the public and private leaderboards for the models described in this post. Table 1: Data sets used for the H2O hackathon in July 2016. From the point of view of training a machine learning algorithm, method 2 may have an advantage — more data records, fewer predictors per record — that makes it easier to optimize. For brevity I will refer to “the training (or testing) subset of the hackathon training data set” as “the training (or testing) subset”. A study of these errors in 2010 is included. This majority of this work explores six statistical models for forecasting and, in particular, a combination model. For method 1, the public leaderboard of the hackathonAfter the hackathon I gained access to the measured turbine powers (as fractions of total capacity) for the public and private testing data sets, see side-note 2. yields: In an attempt to improve on these numbers I’ll use a more powerful machine learning algorithm in the next section. BPA is working with other utilities and wind project owners to develop more accurate long-term and short-term wind forecasts. Short-term wind power forecasts with a prediction horizon from 30 minutes to 72 hours are mainly used for decision-making areas such as Economic Dispatch (ED). • WPF Applications It supplements the basic gradient boosting algorithm with regularization, shrinkage (via a learning rate), data and feature subsampling, and a number of computational optimizations. Its development started in . The bump corresponds to turbines working near full capacity. The forecast anticipates nearly 3 GW of wind growth in 2021 and 2022 as previously contracted projects are completed to meet their PPA obligations. A Generalized Linear Model 6. XGBoost is remarkable in its ability to model “singular” features such as the peak near zero turbine output. One way to take advantage of time series such as the hourly wind speed data set is to compute a rolling window average to smooth out random fluctuations. For example, Greaves et al. Should we apply one-hot encoding? By taking the wind velocity magnitudes as the only predictors, it should be possible to simplify the prediction model considerably and perhaps gain some interpretability. Gradient Boosting with XGBoost 5. Cross-validation was performed with standard K-fold splitting (not time-series splitting). Later I improved on these by doing some feature engineering and using a more powerful learning algorithm. I will also abbreviate mention of “the entire hackathon training data set” into “the training data set”. Thus wind power forecasting is a significant process to provide a tool for the energy reserve scheduling. Typically, shallow trees are used as weak learners, but the algorithm works with any kind of weak learner. The large spike correlates with low wind velocities. The artificial intelligence methods are Artificial Neural Networks (ANNs) and Adaptive Neuro-fuzzy Inference Systems (ANFIS). The ratio between the two wind speeds in the figure varies between about 1.3 and 3.0, suggesting that knowledge of the wind speed at one height provides only limited information about wind speed at another height. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented. Wind speed and direction. This complicates the work of grid operators, energy traders and wind farm owners or operators. (The wind speed units are unknown.) This is done by using numerical weather prediction (NWP) models. The offshore wind market is expected to grow at a much faster pace, while the onshore wind market . The link function ​g must map a number between 0 and 1 to the entire real line. Xcel has the largest wind-energy capacity of any utility in the United States, some 5,700 megawatts. Forecasting Data. Xing Deng. I optimized each turbine separately on a hyperparameter grid, using five-fold cross-validation. The entire hackathon data set contained 168,000 hourly measurements made between January 1, 2012 and December 1, 2013 (100 weeks). In random forest regression one is averaging a large number of regression trees. Python and R Jupyter notebooks for this analysis can be found in my GitHub repository WindTurbineOutputPrediction. At the hackathonA note about terminology: to train models I split the hackathon training data set into a subset for training (typically via cross-validation) and a subset for testing. Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. There are many conventional and artificial intelligence methods that have been developed to achieve accurate wind power forecasting. Based on a large number of historical data of wind farms, the statistical methods use algorithms including Kalman filter [4], autoregressive (AR) model, autoregressive moving average The simplest model for vertical wind profiles is a power law, according to which the ratio of wind speeds at two different heights is a constant. However, there are clear challenges facing the wind power industry and the science behind the . A long term wind forecasting technique is thus required. Also, I’ll be using method 1 from now on in order to avoid dealing with the ZONEID categorical features. In gradient boosting models, a weak learner is improved by the sequential addition of more weak learners. Both steps were performed with XGBoost, but no improvement was obtained. The Hackathon's Challenge 2. This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at SOCO 2011 held in the beautiful and historic city of Salamanca, Spain, April 2011. However the fit to the testing subset does not appear to be worse than that to the training subset, suggesting that whatever features do get captured will generalize. This is shown above for Method 2, but is also true for Method 1. 1,, Chunlong Hu. Although the data set is structured in such a way that each record associates one of ten turbines with only one set of wind velocity measurements (U10, U100, V10, and V100), it is instructive to see how the power output of a given turbine correlates with wind measurements near other turbines. Probabilistic wind power forecasting is the most recent addition to our technological offering. The GAMLSS fitter then uses the entire training subset to produce fitted values for the coefficients ​c_{0}, c_{1},\ldots,c_{p} and ​d_{0}, d_{1},\ldots,d_{p}, as well as for ​\sigma and ​\tau. For the system organization as well as energy dispatching, a wind farm operator should know the power from wind in advance. The main data sources are historical measurement records of the wind farm SCADA system database and meteorological variables of NWP model. The forecast for wind power forecasting. Programme and proceedings. This is more general than residuals and therefore more useful. Finally, given the fitted coefficients, the link functions ​g and ​h, and an instance from the training or testing subset, one can predict values for all four parameters of the distribution of ​Y. A standard choice for this is the logit function: The second parameter I linked is the combination ​\nu of peak probabilities: The link function ​h maps a positive number to the real line. These measurements are spread out over different records but there are no missing data. Would it help to split the machine learning problem into a classification first (zero turbine output versus nonzero turbine output), followed by a regression on the nonzero outputs? Source: Electricity Reliability Council of Texas short-term wind power forecast Load forecasting refers to the prediction of electricity demand. Accurate wind power forecasting is critical for the grid's efficiency, reliability, and sustainability. Peaks in wind speed correspond to peaks in turbine power, and the effect of the cut-in speed is also visible (periods of zero turbine output associated with low, but non-zero wind speed). Weather radar, wind and waves forecast for kiters, surfers, paragliders, pilots, sailors and anyone else. So available wind power can't be known ahead in time. Moreover, in a market environment, the wind power contribution to the generation portofolio becomes important in determining the . Wind power forecasting can be extended to cover following week, which would give additional help for scheduling maintenance plans. In recent years, several investigations and studies have been conducted in this field. By continuing you agree to the use of cookies. We call what we've built the NCAR Wind Power Forecasting System. Since the measurements were taken at one-hour intervals, they are serially correlated. In this work we examine the shape of the persistence model error distribution for ten different wind plants in the Electric Reliability Council of Texas (ERCOT) system over multiple timescales. For simplicity we will choose as new model one that predicts the average of its inputs. Wind Power Forecasting - Application tool. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. This thesis describes performance measures and ensemble architectures for deterministic and probabilistic forecasts using the application example of wind power forecasting and proposes a novel scheme for the situation-dependent aggregation ... We develop these forecasts tailored to our customers' needs. This is probably because of the way gradient boosting works, each tree acting mostly on instances mismodeled by the previous trees. One of the critical challenges of wind power integration is the variable and uncertain nature of the resource. This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries. Each tree is trained on a resampled version of the training data set, and each tree node is split on a random subset of the features. This applies to both commercial players in liberalized power markets and system operators who need to understand the impact of renewable energy production in their portfolio and on the electricity system as a whole. Table 4: Root-mean-square errors on the public and private leaderboards for the models described in this post. The idea of boosting is to keep adding weak learners in this fashion until no further improvement is obtained.