# Autonomous Cyber-Physical Systems Enabling Smart Positive Energy Districts
## 3 Emerging Technologies Enabling Positive Energy Districts
### Artificial Intelligence  
The use of AI for urban planning is an understudied research area [41]. AI facilitates smarter planning, operation, and management of PEDs. Using AI technologies, big data, and urban intelligent systems is vital to develop intelligent urban environments. The aims are to improve urban environment management and smart city quality of life [7; 8]. AI models are considered to be efficient tools for treating complex and immense data. They learn relationships between a system's outputs and inputs [42]. From prediction of energy demand and generation to optimization of energy flows and load management, AI is playing a pivotal role in the transformation of energy infrastructure. For example, AI supports the organization, planning, and operation of smart grids by predicting energy demand and generation, optimizing energy flows, and coordinating energy resource distribution [43]. Through this process, the reliability and resilience of the grid is enhanced and the integration of various energy sources is improved. In the context of energy efficiency optimization, AI can provide continuous improvements of energy-saving strategies. This is possible because of the ability of AI to dynamically adapt and learn from data [44]. AI energy efficiency optimization is based on, e.g., occupant patterns, weather conditions, and energy prices; hence, AI enables reduced energy consumption, with subsequent reduced energy costs and decreased GHGs. AI can also speed up the adoption of renewable energy sources, such as solar and wind, by improvement of forecasting, energy storage optimization, and grid stability management [43]. This results in more efficient, more robust, and more reliable use of renewable resources; hence, it supports the shift towards net-zero energy systems. The configuration and control of energy storage systems with the use of AI ensure stable and economic operation as regards renewable energy [43]. AI's ability to overcome the economic, social, environmental, and governance challenges of cities and districts is one of its main advantages. It is becoming a substantial means of realizing smart and sustainable progress [41]. AI techniques are increasingly used to analyze large amounts of data and extract important insights in PED performance [42]. Various applications, including load predictions, energy pattern profiling, regional energy consumption mapping, building stock measurement and analysis, and retrofit strategy effect analysis are successfully addressed by this kind of approach. AI algorithms can optimize energy use in buildings by analyzing occupancy patterns, weather data, and historical energy usage [45]. Heating, cooling, and lighting systems can automatically adapt in line with real-time conditions. As a result, energy savings are achieved without compromising comfort levels. Two AI techniques, machine learning (ML) and natural language processing (NLP), were examined by [PERSON] et al. [42] to understand their potential for PED element modeling, extracting, and mapping. These tools facilitate the optimization of energy flows, control systems, and design strategies through the analysis of large datasets. The outcomes result in more accurate and actionable insights into the successful planning and implementation of PEDs. Complex environmental data used to identify patterns, predict changes, assess risks, and guide decisions can be analyzed by ML. The results revealed that both ML and NLP, by utilizing vast amounts of data, have significant potential for optimization, control, design, and stakeholder mapping. Hence, they can provide precise and valuable insights to support the planning and implementation of PEDs. AI fosters advancements in energy material and complements innovations in blockchain and the IoT [43].