Abstract: An energy consumption alerting method includes measuring location-specific energy consumption values over a specific period at a sensor deployed at a location of a monitored site, and decomposing the location-specific energy consumption values according to a first characteristic. The method further includes decomposing the location-specific energy consumption values according to a second characteristic, and obtaining a first decomposed energy consumption value. The method additionally includes determining a corresponding first reference value based on the decomposed values, and comparing the first decomposed energy consumption value with the determined corresponding first reference value. Additionally, the method includes notifying a user if the first decomposed energy consumption value and the determined corresponding first reference value differ from each other. Furthermore, an energy consumption alerting system and a cloud-based energy consumption alerting platform is provided.

Abstract: According to one embodiment of the present invention, a prediction system is provided. The system comprises a first data decomposition facility configured to decompose a provided time series of consumption data into a plurality of different training sets for different types of days and a second data decomposition facility configured to decompose each one of the plurality of training sets into at least a seasonal component and a trend component. The system further comprises a regression facility configured to perform a regression analysis on the decomposed consumption data based on at least the trend component and chronological information associated with the consumption data of the respective training set to train a prediction function and a prediction facility configured to estimate predicted energy consumption data based on the trained prediction function and the type of a day for which the prediction is performed.

Abstract: A cloud-based method for identifying a energy profile of an end user is provided. The method includes obtaining information including a total amount of consumed energy and a total equivalent for at least one previous period from at least one listing of the end user, and estimating a consumption profile for a plurality of time slots based on the total amount of consumed energy.

Abstract: According to one embodiment of the present invention, a prediction system is provided. The system comprises a first data decomposition facility configured to decompose a provided time series of consumption data into a plurality of different training sets for different types of days and a second data decomposition facility configured to decompose each one of the plurality of training sets into at least a seasonal component and a trend component. The system further comprises a regression facility configured to perform a regression analysis on the decomposed consumption data based on at least the trend component and chronological information associated with the consumption data of the respective training set to train a prediction function and a prediction facility configured to estimate predicted energy consumption data based on the trained prediction function and the type of a day for which the prediction is performed.

Abstract: A cloud-based method for identifying a energy profile of an end user is provided. The method includes obtaining information including a total amount of consumed energy and a total equivalent for at least one previous period from at least one listing of the end user, and estimating a consumption profile for a plurality of time slots based on the total amount of consumed energy.

Abstract: An energy consumption alerting method includes measuring location-specific energy consumption values over a specific period at a sensor deployed at a location of a monitored site, and decomposing the location-specific energy consumption values according to a first characteristic. The method further includes decomposing the location-specific energy consumption values according to a second characteristic, and obtaining a first decomposed energy consumption value. The method additionally includes determining a corresponding first reference value based on the decomposed values, and comparing the first decomposed energy consumption value with the determined corresponding first reference value. Additionally, the method includes notifying a user if the first decomposed energy consumption value and the determined corresponding first reference value differ from each other. Furthermore, an energy consumption alerting system and a cloud-based energy consumption alerting platform is provided.

Abstract: According to one embodiment of the present invention, a prediction system is provided. The system comprises a first data decomposition facility configured to decompose a provided time series of consumption data into a plurality of different training sets for different types of days and a second data decomposition facility configured to decompose each one of the plurality of training sets into at least a seasonal component and a trend component. The system further comprises a regression facility configured to perform a regression analysis on the decomposed consumption data based on at least the trend component and chronological information associated with the consumption data of the respective training set to train a prediction function and a prediction facility configured to estimate predicted energy consumption data based on the trained prediction function and the type of a day for which the prediction is performed.