Market conditions, global competition and environmental stewardship have created a need for improving energy efficiencies. Therefore, a significant role has emerged for tools and technologies that enable efficient management of energy resources. Technologies and mathematical models for such analyses have existed and are being developed in the fields of data mining and pattern recognition. Implementation of tool sets based on current state of the art techniques developed in the academic world will result in a powerful suite of methods, applications and tools to provide insights into patterns and modes of efficient and inefficient usage and thereby facilitate significant savings of energy. These tools are based on ideas derived from the recent research in the fields of data mining and artificial intelligence. We consider the problem of mining databases containing time series type of energy consumption data for discovering typical temporal characteristics and thus identifying usage patterns. Temporal profiles of a utility (electricity or gas) consumption during a day are recorded and such records for a number of years may be stored in a database. Data mining algorithms described here can determine typical temporal behaviors of utility consumption and the frequency of their occurrence. An interesting pattern is a sequence of observed values that either has a high frequency of occurrence among all the daily profiles, and thus signifies a stable operating mode, or is infrequent but signifies special operating requirements. The capability of these methods to identify operating modes and quantify the duration of these modes provides the ability to objectively specify design requirements and evaluate the energy impact of proposed solutions.