Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side
JJ Cárdenas, L Romeral, A Garcia… - Expert Systems with …, 2012 - Elsevier
Expert Systems with Applications, 2012•Elsevier
This work presents an electricity consumption-forecasting framework configured
automatically and based on an Adaptative Neural Network Inference System (ANFIS). This
framework is aimed to be implemented in industrial plants, such as automotive factories, with
the objective of giving support to an Intelligent Energy Management System (IEMS). The
forecasting purpose is to support the decision-making (ie scheduling workdays, on–off
production lines, shift power loads to avoid load peaks, etc.) to optimize and improve …
automatically and based on an Adaptative Neural Network Inference System (ANFIS). This
framework is aimed to be implemented in industrial plants, such as automotive factories, with
the objective of giving support to an Intelligent Energy Management System (IEMS). The
forecasting purpose is to support the decision-making (ie scheduling workdays, on–off
production lines, shift power loads to avoid load peaks, etc.) to optimize and improve …
This work presents an electricity consumption-forecasting framework configured automatically and based on an Adaptative Neural Network Inference System (ANFIS). This framework is aimed to be implemented in industrial plants, such as automotive factories, with the objective of giving support to an Intelligent Energy Management System (IEMS). The forecasting purpose is to support the decision-making (i.e. scheduling workdays, on–off production lines, shift power loads to avoid load peaks, etc.) to optimize and improve economical, environmental and electrical key performance indicators. The base structure algorithm, the ANFIS algorithm, was configured by means of a Multi Objective Genetic Algorithm (MOGA), with the aim of getting an automatic-configuration system modelling. This system was implemented in an independent section of an automotive factory, which was selected for the high randomness of its main loads. The time resolution for forecasting was the quarter hour. Under these challenging conditions, the autonomous configuration, system learning and prognosis were tested with success.
Elsevier
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