Abstract: The method of forecasting for solar-based power systems (10) recognizes that no single solar irradiance forecasting model provides the best forecasting prediction for every current weather trend at every time of the year. Instead, the method trains a classifier to select the best solar irradiance forecasting model for prevailing conditions through a machine learning approach. The resulting solar irradiance forecast predictions are then used to allocate the solar-based power systems (10) resources and modify demand when necessary in order to maintain a substantially constant voltage supply in the system (10).

Abstract: The method of forecasting for solar-based power systems (10) recognizes that no single solar irradiance forecasting model provides the best forecasting prediction for every current weather trend at every time of the year. Instead, the method trains a classifier to select the best solar irradiance forecasting model for prevailing conditions through a machine learning approach. The resulting solar irradiance forecast predictions are then used to allocate the solar-based power systems (10) resources and modify demand when necessary in order to maintain a substantially constant voltage supply in the system (10).

Abstract: Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

Abstract: Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture are described according to some aspects. In one aspect, a hypothesis analysis method includes providing a hypothesis, providing an indicator which at least one of supports and refutes the hypothesis, using the indicator, associating evidence with the hypothesis, weighting the association of the evidence with the hypothesis, and using the weighting, providing information regarding the accuracy of the hypothesis.

Abstract: Methods of defining ontologies, word disambiguation methods, computer systems, and articles of manufacture are described according to some aspects. In one aspect, a word disambiguation method includes accessing textual content to be disambiguated, wherein the textual content comprises a plurality of words individually comprising a plurality of word senses, for an individual word of the textual content, identifying one of the word senses of the word as indicative of the meaning of the word in the textual content, for the individual word, selecting one of a plurality of event classes of a lexical database ontology using the identified word sense of the individual word, and for the individual word, associating the selected one of the event classes with the textual content to provide disambiguation of a meaning of the individual word in the textual content.

Abstract: Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

Abstract: A method and system are provided for matching a reference document with a plurality of corpus documents. Semantic content is derived from the reference document according to a hierarchical arrangement of semantic types. For each corpus document, semantic content is also derived from the corpus document according to the hierarchical arrangement of semantic types. A matching score is produced for each corpus document by determining a relatedness between the corpus document and the reference document. This relatedness is derived from the respective semantic contents of the two documents. The corpus documents may be ranked in accordance with the determined matching scores.