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.

In recent years, environmental sustainability has received widespread attention due to continued depletion of natural resources and degradation of the environment. Life cycle assessment (LCA) is a methodology for quantifying multiple environmental impacts of a product, across its entire life cycle — from creation to use to discard. The key object of interest in LCA is the inventory tree, with the desired product as the root node and the materials and processes used across its life cycle as the children. The total impact of the parent in any environmental category is a linear combination of the impacts of the children in that category. LCA has generally been used in "forward: mode: given an inventory tree and impact factors of its children, the task is to compute the impact factors of the root, i.e., the product being modeled. We propose a data mining approach to solve the inverse problem, where the task is to infer inventory trees from a database of environmental factors. This is an important problem with applications in not just understanding what parts and processes constitute a product but also in designing and developing more sustainable alternatives. Our solution methodology is one of feature selection but set in the context of a non-negative least squares problem. It organizes numerous non-negative least squares fits over the impact factor database into a set of pairwise membership relations which are then summarized into candidate trees in turn yielding a consensus tree. We demonstrate the applicability of our approach over real LCA datasets obtained from a large computer manufacturer.