# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
### Literature in Smart Buildings  
On the side of AI applications in buildings, several aspects, such as financially and technically, are considered important. Smart building systems are enabled by AI technologies, which can optimize energy consumption by adjusting lighting, heating, and cooling in real time based on occupancy and utilization patterns. This reduces energy costs and environmental impact. AI-driven predictive maintenance can forecast equipment failures and facilitate timely repairs, minimizing downtime and prolonging the lifespan of building infrastructure. Additionally, AI-enhanced security systems have advanced surveillance and threat detection features, ensuring a safer environment for occupants. The AI integration makes buildings more responsive, adaptable, and efficient, aligning with the demands of modern urban living and aiding in the development of more resilient and intelligent cities. Research in [31] reviews ML methods for predicting energy demand in modern buildings, focusing on their accuracy and efficiency in minimizing energy consumption. Furthermore, a systematic review in [32] evaluates smarter cities and artificial IoT (AIoT) solutions aimed at promoting green energy and sustainability. This review investigates the foundational principles of these cities, their relationships with urbanism paradigms, green energy transition, data-driven technologies/methodologies, key factors, essential AI and AIoT solutions, their role in advancing green environmental practices, and their implementation challenges. The gained perspectives enhance the potential of AI and AIoT technologies to improve sustainable urban development practices and further integrate eco-urbanism with AI- and AIoT-driven urbanism. In addition to simulation tools, building energy management (BEM) is important too, and these tools are for evaluating building performance. Accordingly, the review conducted in [33] manifested various modeling approaches, including white box, black box, and web tool models, in addition to the simulation scales. The goal is to identify the advantages and weaknesses of the proposed strategies. The study [34] reviews techniques based on ML and DL for managing building energy systems and evaluating their effectiveness. The findings indicate that hybrid and ensemble methods have higher robustness, while DL-based, hybrid, and ensemble-based models achieve the highest robustness scores. In contrast, ANN, SVM, and individual ML models demonstrate varying levels of robustness. Also, an overview of interpretable ML techniques is reviewed in [35] for BEM on both ante-hoc and post-hoc approaches. The challenges include varying terminologies, difficulties in comparing the performance of interpretable ML methods, and the limited interpretability of current techniques. Future research should be more focused on enhancing the interpretability of black-box models. Improving energy efficiency in the building sector is important for reducing CO\({}_{2}\) gas emissions and fossil fuel consumption. Early design considerations, energy management, and smart renovations can facilitate this goal. Various AI and machine learning techniques, including NNs, SVMS, gradient boosting regressors (GBRs), and clustering methods, can be utilized to forecast and enhance building energy performance, as reviewed in [36]. On the other side, optimal planning and the associated methods are necessary to enhance home EMS (HEMs) to meet demand requirements [37; 38; 39]. Accurate load forecasting and scheduling are also important components of HEMs. ML techniques have been taken into account for smart HEM and for autonomously controlling heating and domestic hot water systems. The growing number of homes equipped with RES is increasing too for optimizing energy usage. AI technologies can be taken into account to reduce energy usage through improved control systems. Future research should investigate the role of AI during the construction phase for considering energy-efficient facility construction systems techniques and control strategies.