One of the main uses of digital transformation is the energy transition. Artificial intelligence can be used as a predictive tool, for example to predict the consumption of electrical energy or the health of a component or machine. But predicting exactly is complicated.
Excerpt from The Artificial Intelligence article as part of Moamar’s digital transformation SAYED MOUCHAWEH
Digital transformation in the context of the energy transition faces several challenges. First, replacing fossil fuel power plants with renewable energy resources significantly increases the uncertainty and complexity of ensuring grid stability and efficient energy management. This is due to the heavy dependence betweenrenewable energy production and weather conditions.
In addition, management to reduce energy consumption, energy prices or grid congestion must be done online by processing huge volumes of sequential data on aggregate energy consumption, online energy prices, contextual and physical parameters, and weather data. In addition, this energy management must take into account future events that may occur in the smart grid,such as the addition of new buildings, the use of new technologies, etc.
Finally, security and privacy risks must be taken into account due to increasing connectivity and data exchange between users (consumers also becoming producers, called consumers) and other players in the electricity grid (utilities, operators, service providers, etc.). The different AI-based techniques and methods used to meet these challenges are presented here according to three criteria: their purpose (exit), the learning mode they used to provide this goal (exit) and their scope of application.
In the area of energy transition, AI-based methods are primarily used to predict the consumption or demand of electrical energy, the energy produced by a renewable energy resource such as wind turbines or photovoltaic panels, and the health of a component or machine such as insulators, transformers, generators or transmission lines.
Forecasting in the area of energy transition is difficult due to fluctuations in demand and production due to variability in weather conditions, building characteristics and thermal properties of physical materials used, appliances (such as heating, ventilation and air conditioning), ageing, user behaviour, etc.
This prediction becomes more difficult when the time horizon is long-term and the resolution is higher. Therefore, the AI-based methods used for prediction in the field of energy transition differ depending on their prediction accuracy as well as the time horizon and resolution used to make the prediction. Accuracy can be assessed using two criteria primarily: the average quadratic error (EQM) and the Pearson correlation coefficient (PCC). The EQM indicates the difference between actual and predicted values during a time window while the CCP indicates the degree of linear dependence between actual and predicted values during a time window.