In recent years, energy information systems have had an important role in the operational optimization of intelligent buildings to provide such benefits as high efficiency, energy savings and smart services. Interest in the intelligent management of home energy consumption using data mining and time series analysis is increasing. Therefore, this work develops an efficient Web-based energy management system for the power consumption of home appliances, which monitors the energy load of a home and analyzes its energy consumption based on data from smart meters and sensors, then sends information to various stakeholders. It interacts with the end-user through energy dashboards and emails. The Web-based system includes a hybrid artificial intelligence system to improve its prediction of energy usage. An automatic warning function is also developed to identify anomalous energy consumption in a home in real time. The system automatically sends a message to the user's email whenever a warning is necessary. End-users of this Web-based system can use forecast information and anomalous data to enhance the efficiency of energy usage in their buildings especially during peak times by adjusting the operating schedule of their appliances and electrical equipment.

In recent years, energy information systems have had an important role in the operational optimization of intelligent buildings to provide such benefits as high efficiency, energy savings and smart services. Interest in the intelligent management of home energy consumption using data mining and time series analysis is increasing. Therefore, this work develops an efficient Web-based energy management system for the power consumption of home appliances, which monitors the energy load of a home and analyzes its energy consumption based on data from smart meters and sensors, then sends information to various stakeholders. It interacts with the end-user through energy dashboards and emails. The Web-based system includes a hybrid artificial intelligence system to improve its prediction of energy usage. An automatic warning function is also developed to identify anomalous energy consumption in a home in real time. The system automatically sends a message to the user's email whenever a warning is necessary. End-users of this Web-based system can use forecast information and anomalous data to enhance the efficiency of energy usage in their buildings especially during peak times by adjusting the operating schedule of their appliances and electrical equipment.

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