Solar Nowcasting with Cluster-based Detrending

Transcription

1 Solar Nowcasting with Cluster-based Detrending Antonio Sanfilippo, Luis Pomares, Daniel Perez-Astudillo, Nassma Mohandes, Dunia Bachour ICEM 2017 Oral Presentation 26-29June 2017, Bari, Italy

2 Overview Problem Statement Background Hypothesis Approach & Data Results Next Steps

3 Problem Statement Solar forecasting is crucial in managing PV integration Forward commitment of generation units (intra-day and day ahead) Variable generation ramps (minutes/hours ahead) PV integration in the power distribution system Transmission congestion management Energy trading. and more Challenges for Qatar: Variability due to: Cloudiness during half of the year Micro-climates due to sea-land climatic interactions Aerosols in the atmosphere Emissions from industrial and urban land use High loads of dust in the atmosphere

4 Solar variability in Qatar by hour & day of the year 2014 QEERI dataset from Villa F

5 How to improve solar forecasting? Use stochastic models for 6 hours predictions, and physics-based models for longer predictions 1 Use multi-modeling 2 and ensemble machine learning 3 to combine stochastic models Integrate predictions from physics-based models into stochastic models De-trending our focus in this study Group time series data into coherent subsets to train more accurate stochastic solar forecasting models QUESTION: WHAT IS THE BEST DE-TRENDING METHOD? The use of multi-model classifiers 1 Diagne et al. 2013, Inman et al. 2013; 2 Sanfilippo et al. 2015; 3 Lauret et al 2012

6 Hypothesis Using data mining techniques to cluster solar time series data creates datasets that have stronger internal coherence as compared to other approaches Training datasets with stronger internal coherence help training more accurate forecasting models

7 Approach Partition time series of solar irradiance data according to variability using season-based and clustering methods Assess the relative performance of each de-trending method by evaluating the same forecasting algorithms with different data partitions Develop a technique to identify the class of each solar irradiance time series so that each can be matched with the appropriate forecasting solution

8 De-trending approaches 1. K-means clustering 2. X-Means clustering 3. Cascade Simple K-Means clustering 4. M-Tree clustering 5. EM (expectation maximization) clustering 6. LVQ clustering

9 Forecasting Focus: Near-real time Use regression to learn model coefficients which provide the basis for prediction by measuring the relation between an observation at time t and observations at previous times Persistence Autoregressive models. AR(3) and AR(11). NN1.. NN5. (Feedforward NN) ARNX (Autoregressive network with exogenous inputs) RNN (Layer recurrent neural network)

10 Data collection equipment Radiometric ground monitoring station Secondary Standard Pyranometers for measuring GHI and DHI First Class pyrheliometer for measuring DNI Installed on rooftop of office building in Doha s Education City Lat: 25.33o N, Lon: 51.42o E Daily maintenance

11 Data Collected 5-minute averages of direct (DNI), horizontal (DHI) and global (GHI) irradiance measured in W/m2 over one year (2014) Baseline Surface Radiation Network and Long quality control checks Extremely rare limits, physical limits, consistency checks Other advance filters developed to address limitations of BSRN

12 Choosing a forecasting target: Ktp GHI is the relevant measure for PV (Pelland et al. 2013) The Clearness Index (Kt) is used to quantify the impact of the atmosphere on GHI Kt = ratio of GHI to the corresponding irradiance out of the atmosphere Normalize Kt (Ktp) to alleviate the dependence of Kt on zenith angle Normalize Kt with respect to a standard clear-sky global irradiance profile for a relative air mass of one

13 Novel de-trending approach based on data mining Use clustering to detect latent classes in solar irradiance time series data, and classification to evaluate the detected classes Use expectation maximization to cluster the QEERI 2014 dataset of 5-minute averages of normalized clearness index Month Day Hour Min KTp 1 KTp Train & evaluate a Bayesian classifier with the clustered dataset

14 A Data-mining approach to de-trending Clustering BN Classification Full year solar irradiance dataset Cluster 1 Cluster 2 Cluster 3 Cluster 4 75% of cluster data 25% of cluster data Train classifier that assigns each record to its cluster Evaluation: 96% F1 F1 = 2 precision recall precision + recall precision = recall = TPs TPs + FPs TPs TPs + FNs Apply classifier: Cluster 2

15 Forecasting with de-trending Train and evaluate Autoregressive AR11, AR3 Artificial Neural Network (ANN), NARX and RRN models that provide 12 steps-ahead predictions at 5 minutes intervals Use two types of de-trended datasets for training 4 seasons 6 type of clusters. 4 clusters for all for EM cluster Train & evaluate models with full year dataset to verify the effects of de-trending Use persistence as baseline for model comparison Use several evaluation metrics only rrmse shown here

16 Forecasting results for the whole year ERRORS MBE RMSE MAE rmbe rrmse rmae NARX RNN ANN ANN ANN ANN ANN AR(11) AR(3) PER

17 Forecasting results for the whole year

18 Forecasting results with de-seasoning ERRORS MBE RMSE MAE rmbe rrmse rmae NARX RNN ANN ANN ANN ANN ANN AR(11) AR(3) PER

19 Forecasting results with de-seasoning

20 Forecasting results with all clusters. 4 groups Average rrmse (%) KMeans EM VQ MTree Cascade Kmeans XMeans NARX RNN ANN ANN ANN ANN ANN PER 19.63

21 Forecasting results with all clusters. 4 groups Average rrmse (%) KMeans EM VQ MTree Cascade Kmeans XMeans NARX RNN ANN ANN ANN ANN ANN PER 19.63

22 Forecasting results with EM cluster and NARX Errors for EM cluster and NARX MBE RMSE MAE rmbe rrmse rmae Whole year Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster PER

23 Forecasting results with EM cluster and NARX Errors for EM cluster and NARX MBE RMSE MAE rmbe rrmse rmae Whole year Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster PER

24 Forecasting results with EM cluster and NARX % RRMSD NUMBER OF CLUSTERS

25 Forecasting results with clustering de-trending Overall NARX performs significantly better with the whole data set, seasons and clusters Non-stationary data concentrated in a single cluster (3). AR model is strongly affected by time series discontinuity (clusters 2-4) Average rrmse Whole year Cluster1 Cluster2 Cluster3 Cluster4 NARX 8.56% 3.80% 11.32% 17.04% 4.83% AR(11) 18.69% 2.42% 47.95% % % Persistence 19.63% Average standard deviation By horizon 2.04% 3.62% 18.38% 1.63% By time series 0.77% 2.10% 10.01% 0.76%

26 Season-based vs. Clustering de-trending Clustering de-trending helps separate the clusters with higher complexity The average rrmse across cluster 1-2 results for the NARX model is lower than the average rrmse across season and whole year results Solar forecasting error with season vs. clustering de-trending (NARX) All Year Season1 Season2 Season3 Season4 Avg. S1-S4 11.3% 9.12% 7.96% 10.8% 9.80% 8.56% Cluster1 Cluster2 Cluster 3 Avg. C1-C3 4.44% 8.10% 16.08% 9.94% The concentration of variability in the same cluster (3) may help find solutions for further performance improvements

27 Conclusions Solar forecasting is needed to manage PV integration Statistical and AI approaches can be useful, but no single model can provide the best performance for all inputs Clustering de-trending provides an optimal divide and conquer technique to improve solar forecasts but other instruments like sky cameras needs to be used to improve the predictions of the clusters with higher complexity Use cluster de-trending as a diagnostic to identify data partitions for which more complex modeling techniques are needed

28 Next steps 1. Use the predictions of the clusters with a classifier

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