# Autonomous Cyber-Physical Systems Enabling Smart Positive Energy Districts
## 3 Emerging Technologies Enabling Positive Energy Districts
### Artificial Intelligence
Nonetheless, there remain challenges associated with the use of AI, such as ethical perspectives associated with the automation of the energy sector and autonomous decision-making, technical and economic issues (e.g., scalability, intermittency of renewable energy, and costs of infrastructure positioning), and regulatory issues associated with its application [46; 47]. Other challenges include the accessibility and quality of data, system interoperability, and the complexity of energy data analysis [20; 48]. Practical application cases for the use of AI in PEDs include the following:  
1. _Energy Demand Forecasting:_ AI models use occupancy levels, weather data, and historical usage to forecast building energy consumption in real time using machine learning and Convolutional Neural Networks (CNNs). This enables dynamic energy balancing and reduces peak loads [49]. 2. _Renewable Energy Optimization:_ AI predicts the optimal slope and orientation of solar panels based on seasonal and daily weather patterns for increased energy production and efficiency. 3. _Smart Energy Management Systems (SEMS):_ AI-based systems autonomously control heating, ventilation, lighting, and appliances and adapt building climate control systems accordingly by also taking occupancy predictions into consideration to minimize energy waste and maximize comfort. 4. _Energy Storage Management:_ AI coordinates when to use stored energy versus grid electricity for cost saving and optimizes charging/discharging of batteries based on real-time supply and demand for enhanced energy reliability and cost efficiency. 5. _Peer-to-Peer (P2P) Energy Trading:_ AI algorithms facilitate decentralized energy trading among PED members. Blockchain and AI work together to enhance local energy exchange and improve grid independence. This is enabled by automating micro-transactions between households (e.g., using solar panels). 6. _Grid Interaction and Flexibility Services:_ AI manages district-level battery storage to respond to real-time grid signals and predicts when and how much energy can be fed into or drawn from the grid. Hence, AI supports grid stability and participation in demand response programs. * _Building Information Modeling (BIM) and AI:_ AI identifies energy efficiency improvements in district planning stages. AI can be used with BIM to simulate and optimize energy use in new or retrofitted buildings for improved building performance design before construction. * _Predictive Maintenance:_ AI detects anomalies in energy systems (e.g., HVAC, solar inverters, etc.) by indicating performance drops in solar panels due to dirt accumulation for reduced downtime and extends equipment life. * _Mobility and Electric Vehicle (EV) Integration:_ AI directs EVs to charge during solar peak hours and discharge during peak demand and optimizes EV charging schedules based on district energy supply and demand to avoid grid overloads and use surplus renewable energy. * _Citizen Engagement and Behavior Change:_ Personalized AI insights show how a household's actions contribute to the district energy surplus and AI-driven apps and dashboards provide feedback to enhance awareness of and engagement in achieving PED goals. For example, speech emotion recognition (SER) could be deployed to recognize occupants' emotional feelings and dynamically adjust the system accordingly [50].