Artificial intelligence has the potential to cut energy waste and lower energy costs worldwide
The use of AI in the power sector is now reaching emerging markets, where it may have a critical impact,
as clean, cheap, and reliable energy is essential to development. The challenges can be addressed over time by transferring knowledge of the power sector to AI software companies. When designed carefully, AI systems can be particularly useful in the automation of routine and structured tasks, leaving humans to grapple with the powerful challenges of tomorrow. Artificial intelligence has the potential to cut energy waste, lower energy costs, and facilitate and accelerate the use of clean renewable energy sources in power grids worldwide. AI can also improve the planning, operation, and control of power systems. Thus, AI technologies are closely tied to the ability to provide clean and cheap energy that is essential to development. This article lists the top use of artificial intelligence in the electric power sector.
Power Load Forecasting
At present, artificial neural network technology has become one of the commonly used methods to predict power load. In the early stages, the backpropagation algorithm (BP algorithm) used by the artificial neural network is a model containing only one hidden node. In the case of limited samples and computational elements, the model cannot fully describe complex functions. With the advance of technology, deep learning is widely used in power system load forecasting.
Power Generation Forecast of Renewable Energy
The traditional shallow model prediction method has poor prediction performance when dealing with nonlinear and non-stationary wind or light data. Similar to the load prediction principle, AI can also be effectively applied to the power prediction of wind power and photovoltaic power generation. In addition, other deep learning methods have also been tested in the power prediction of renewable energy generation.
AI is also helping grid operators reduce their overall carbon footprint in several ways. One way involves the use of cognitive computing, based on AI and signal processing technologies, and neural networks, computer systems patterned after the networks in a human brain.
Maintenance Facilitated by Image Processing
The United Kingdom’s National Grid has turned to drones to monitor wires and pylons that transmit electricity from power stations to homes and businesses. Equipped with high-resolution still and infrared cameras, these drones have been particularly useful in fault detection due to their ability to cover vast geographical areas and difficult terrain.
Energy Efficiency Decision Making
Smart devices such as Amazon Alexa, Google Home, and Google Nest enable customers to interact with their thermostats and other control systems to monitor their energy consumption. The digital transformation of home energy management and consumer appliances will allow automatic meters to use AI to optimize energy consumption and storage.
Balancing Power Supply and Demand
AI technology is playing an increasingly vital role in managing the electric grid to ensure that there is power available when and where it’s needed. This is going to become more important in the future as the electricity demand is expected to rise, with consumers increasingly purchasing smart devices able to transmit and receive data via the Internet, commonly known as the Internet of Things.
Analysis of Consumer Electricity Consumption Behavior
The clustering and identification ability of machine learning in AI can be utilized to analyze the power
consumption behavior of users, detect abnormal power consumption and non-invasive load monitoring. These analyses and tests provide theoretical support for the reasonable pricing of a comprehensive energy system and the improvement of energy structure and support two-way flexible interaction between energy supply and users.
Prevention of Losses Due to Informal Connections
Losses due to informal connections constitute another challenge for the power sector. AI could be used to spot discrepancies in usage patterns, payment history, and other consumer data to detect these informal connections. Furthermore, when combined with automated meters, it can improve monitoring for them. It can also help optimize costly and time-consuming physical inspections.
Power network security protection
Deep learning can automatically identify network attack features, detect malware and intrusion, and provide network security protection for the power systems. The probability of a power system being attacked is far less than that of normal operation, so the abnormal sample data of power networks being attacked is far less than that of normal sample data. The training process of deep learning does not require sample labels, which can mitigate the impact of insufficient sample size.
Fault prediction has been one of the major applications of artificial intelligence in the energy sector, along with real-time maintenance and identification of ideal maintenance schedules. In an industry where equipment failure is common, with potentially significant consequences, AI combined with appropriate sensors can be useful to monitor equipment and detect failures before they happen, thus saving resources, money, time, and lives.