Toward Sustainable Campus Energy Management: A Comprehensive Review of Energy Management, Predictive Algorithms, and Recommendations

Rapid growth and challenges are likely to be experienced by energy generation, delivery, and consumption in the upcoming years, which in turn affect the economic and environmental perspectives. University buildings account for a significant portion of global energy consumption and associated CO₂ emissions, and this is expected to rise substantially in the near future. Unawareness of energy efficiency in academic buildings results in weak sustainability financially and environmentally. This paper aims to review the existing studies related to energy management, efficiency, prediction, and recommendations in university buildings. Various works and algorithms were discussed addressing the challenges and limitations in the existing systems, and proposing insights as an attempt to fill the gap in this significant research domain. Additionally, the limitations of current systems, which offer only short-term solutions, become evident over time. These systems are ineffective in the long run as they lack predictive capabilities that could guide users toward predefined savings goals, actions, recommendations, or established energy standards. The paper states that to facilitate energy efficiency and manage consumption, it is important to extract patterns of energy consumption by data modelling and predictive algorithms to achieve the ultimate goal of consumption recommending and advising. This data driven decisions can support the reduction of energy load which helps in having more sustainable infrastructure and ensures less economic and financial expansion. Practically, the main objective is to support universities to save energy, reduce electricity bills, and maintain people comfort. This paper is beneficial to researchers that have interests to conduct future studies related to energy efficiency, management, prediction, and recommendations. This review study proposes a significant solution for smart buildings that fulfils energy efficiency with minimal cost and efforts.