LAHSASNA , AdelBOUAKKAZ , Abderraouf2024-04-082024-04-082023http://dspace.univ-skikda.dz:4000/handle/123456789/808In the face of global energy challenges, the ability to adapt and innovate is crucial for humanity's survival and progress. Effective energy management plays a pivotal role in optimizing energy consumption patterns and enhancing efficiency. This involves implementing strategies such as energy conservation practices, adopting energy-efficient technologies, and leveraging smart grid systems for improved monitoring and control. To address these goals, this study proposes a decision support system with an integrated information system for energy management in residential homes. By utilizing various machine learning algorithms, the system aims to predict energy consumption and cost, ultimately optimizing energy performance. The performance and effectiveness of these algorithms are thoroughly evaluated, highlighting the potential benefits of machine learning in achieving energy efficiency and cost reduction within homes.enMaster degree theses