AI’s role in predictive analytics for energy savings helps Smart Cities manage resources more efficiently, say experts
As the Internet of Things (IoT), cyber-physical systems and Artificial Intelligence (AI) are deployed in developing smart cities, it becomes more and more imperative to efficiently manage energy resources developing in burgeoning cities.
Researchers at the Technological Institute of Monterrey (Mexico) and Massachusetts Institute of Technology (US) did a study reviewing existing literature, focusing on AI technologies in generation, transmission distribution and consumption, to see what trends are emerging.
They found that AI-powered smart energy systems can analyse large datasets from renewable sources, consumption patterns, weather, and demand forecasts.
This enables optimised energy distribution and reduced dependence on fossil fuels. AI also supports better integration of renewables into the grid by adapting to real-time data and adjusting distribution and storage strategies, especially for variable sources like wind and solar.
The researchers highlight AI’s role in predictive analytics to anticipate demand fluctuations and identify opportunities for energy savings. This helps Smart Cities manage resources more efficiently, reduce waste, and cut carbon emissions.
AI applications in energy systems enable cost savings, control functions, and improved forecasting. It also enhances grid monitoring and demand prediction, boosting operational efficiency and sustainability in Smart Cities and beyond.
Generation and the use of AI
Energy generation forecasting, particularly of alternative sources, emerges as one of the main topics identified.
Selected articles delve into the application of AI methodologies for predicting energy generation from renewable and sustainable sources.
Methodologies described include:
- A method based on non-parametric regression models that forecasts the demand and generation of energy with information from smart meters.
- A physics-informed AI applied to forecast wind power generation, with information on a wind farm in China and machine learning methods.
- Combining fuzzy logic systems to enhance the efficiency of solar energy applications – this approach could be applied to investigate various renewable energy sources.
- An application for Maximum Power Point Tracking (MPPT) of photovoltaic (PV) panels, which is the point where solar panels produce the maximum energy possible and the tracking of this point increases their energy efficiency.
- An application in solar energy in which an Artificial Neural Network (ANN) is applied to create a decision-making tool based on the generation and consumption of solar PV systems that can aid decision makers in creating strategies towards energy generation. These strategies include reducing costs and maximising solar energy generation.
These methodologies show how AI helps to manage large amounts of data to predict these forecasting models and also manages generation data to track the MPPT for PV panels.
In addition, AI has been applied in decision-making tools that consider generation and consumption to provide alternatives that can bring benefits in terms of economic and energy efficiency.
Transmission and the use of AI
On the transmission side, the researchers picked up various concepts that are being researched related to energy transmission lines in Smart Cities, particularly around the introduction of smart grids.
One of the research papers they looked at stated “that in order to have an appropriate electric transmission, an anomaly detection system is required to avoid power losses.”
This article proposed an AI method based on neural networks to detect anomalies for electricity theft detection in smart grids, so the AI could detect the problem and report it.
Also reviewed was a paper suggesting methodologies for demand side management applications that could impact on the performance of transmission networks. This included a mixture between software and hardware to give real-time data, with valuable precision, energy consumption which can be used for monitoring purposes and home area networks which are used to connect electrical devices at home.
Distribution and the use of AI
Some of the articles investigated discussed the heating load of buildings, while others focused on electric vehicles. The ones that focused solely on energy distribution show some key findings for energy distribution strategies.
Methodologies included:
ANNs and machine learning algorithms such as Support Vector Machine to train energy prediction models that could contribute to obtaining the amount of energy consumption of buildings in Smart Cities. This would help distribution systems to be more efficient.
A multi-agent-based simulation of the distribution network of a German city to reach the objective of successfully simulating the dynamic model of the city energy network.
Developing an algorithm to solve energy fraud detection to minimise energy loss on the electricity grid. The algorithm used Convolutional Neural Networks and Robot Process Automation to detect precise fraud in the electricity network, separate from other anomalies that could be present on the network.
Energy consumption and the use of AI
Common topics among reviewed articles focusing on consumption were forecasting, data mining, economy, energy management, user profiling, behaviour modelling, electric vehicles and computing.
Specifically, methodologies described:
- Using smart meter time series from generation sources, the electric grid and localised buildings to build up a Digital Twin of the entire system, with the added benefit of allowing geospatial information to be fed to the twin. The results show that when geospatial data is not available, a 7% overestimation of the grid level is performed during the summer days.
- Forecasting energy consumption of individual residential households through a case study presenting a neural network architecture which can incorporate historical supply and demand, weather data and date information to forecast energy consumption.
- Building energy management to minimise power consumption, examining the application of deep learning structures. Initially, sensory neurons are spread throughout the smart building, collecting data from the environment. Subsequently, a reinforcement learning algorithm is employed to predict values and trends, thereby aiding the building managers with the decision-making task.
The last proposal demonstrated that AI within smart buildings allows for real-time monitoring and accurate predictions of its variables.
Research gaps in the use of AI
The researchers also identify key gaps in current AI energy applications, noting that while AI effectively forecasts solar and wind output, predicting energy generation under changing environmental conditions remains a challenge.
Also, more advanced systems are needed to dynamically adapt to demand and integrate with traditional grids—especially in hybrid energy systems within Smart Cities. In transmission and distribution, AI aids smart grid management and energy loss detection, yet struggles with optimising flow in complex, large-scale networks. More sophisticated algorithms are needed for managing distributed energy across vast areas.
On the consumption side, AI helps reduce usage, forecast demand, and manage building systems. However, real-time personalisation based on user behaviour is still developing and could greatly enhance efficiency.
There is also limited AI application in less-studied renewable sources like hydropower, geothermal, and bio-energy, with research focused mainly on solar, wind, and hydrogen. Finally, AI is often applied in silos.
A unified, end-to-end AI framework is needed to manage the entire energy lifecycle—from generation to consumption—essential for building truly smart, efficient cities.
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Autor(en)/Author(s): Herman Fourie
Quelle/Source: ESI Africa, 09.07.2025
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