| 摘要 |
An energy management system includes a neural network, a predictive model, and a dictionary reducer. The network iteratively calculates weights, resulting in a final set, for each of a plurality of single-word terms and trigram terms taken from training data business names, where each of the weights is indicative of a likelihood of correlating one of a plurality of business categories. The predictive employs sets of the weights to predict a first corresponding one of the plurality of business categories for each of the training data business names until employment of the final set accurately predicts a correct business category for the each of the training data business names, and subsequently employs the final set of the weights to predict a second corresponding one of the plurality of business categories for each of a plurality of operational business names. The dictionary reducer eliminates unessential terms taken to determine the plurality of single-word terms and trigram terms. |
| 主权项 |
1. An energy management system, for communicating with one or more buildings for purposes of managing energy consumption of devices within the buildings, the energy management system comprising:
a neural network, configured to iteratively calculate weights, resulting in a final set of said weights, for each of a plurality of single-word terms and trigram terms taken from training data business names, wherein each of said weights is indicative of a likelihood of correlating one of a plurality of business categories, and wherein said trigram terms each comprise three consecutive ones of said single-word terms as used in said training data business names; a predictive model, coupled to said neural network, configured to iteratively employ sets of said weights to predict a first corresponding one of said plurality of business categories for each of said training data business names until employment of said final set of said weights accurately predicts a correct business category for said each of said training data business names, and configured to subsequently employ said final set of said weights to predict a second corresponding one of said plurality of business categories for each of a plurality of operational business names; and a dictionary reducer, coupled to said neural network, configured to eliminate unessential single-word terms and trigram terms taken from said training data business names to determine said plurality of single-word terms and trigram terms, wherein said plurality of single-word terms and trigram terms are essential to predicting said correct business category for said each of said training data business names. |
