Fine-Grained Photovoltaic Output Prediction Using a Bayesian Ensemble

By Prithwish Chakraborty, Manish Marwah, Martin F. Arlitt, Naren Ramakrishnan. 2012

49 citations recorded by the source corpus.

Local and distributed power generation is increasingly relianton renewable power sources, e.g., solar (photovoltaic or PV) andwind energy. The integration of such sources into the power grid ischallenging, however, due to their variable and intermittent energyoutput. To effectively use them on alarge scale, it is essential to be able to predict power generation at afine-grained level. We describe a novel Bayesian ensemble methodologyinvolving three diverse predictors. Each predictor estimates mixingcoefficients for integrating PV generation output profiles but capturesfundamentally different characteristics. Two of them employ classicalparameterized (naive Bayes) and non-parametric (nearest neighbor) methods tomodel the relationship between weather forecasts and PV output. The thirdpredictor captures the sequentiality implicit in PV generation and uses motifsmined from historical data to estimate the most likely mixture weights usinga stream prediction methodology. We demonstrate the success and superiority of ourmethods on real PV data from two locations that exhibit diverse weatherconditions. Predictions from our model can be harnessed to optimize schedulingof delay tolerant workloads, e.g., in a data center.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

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Citations in the corpus

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017, methodology): “Time series prediction algorithms have been widely applied in many areas, e.g. , ﬁnancial market prediction [Wu et al. , 2013], weather forecasting [Chakraborty et al. , 2012], and complex dynamical system analysis [Liu and Hauskrecht, 2015].”