The energy sector has undergone a significant transformation with new technologies like electric vehicles and home automation. The paradigm shift brought about by covid-19 must be added to all of this.
FREMONT, CA: Recent news items and headlines indicate that energy efficiency and sustainability are no longer trivial issues. The energy industry has undergone significant changes with the introduction of technology such as electric vehicles and home automation. Covid-19 brought about a paradigm shift to all of this. In the wake of the pandemic, teleworking, online learning, and remote access to services have increased dependence on the electricity grid.
As is typically the case when enormous volumes of data are involved, it is evident that AI plays a prominent role.
Applications of AI in the field of energy efficiency and sustainability
Waste management: We have experienced rapid population growth and urbanization in recent decades. In turn, this growth has led to an increase in waste generation. In this regard, efficient and sustainable collection services have become a priority.
Concurrently, the advent of the Internet of Things (IoT) has enabled near-complete communication with all types of devices. Concerning urban furniture, sensor technology allows data collection in real-time from these elements.
Building and home automation: The IoT has allowed enhancing data accessibility. In this instance, it enables the design of environmentally friendly buildings. Sensors in meters, household appliances, and other components of contemporary structures allow the acquisition of real-time readings that feed AI-based algorithms. Using these algorithms makes it possible to estimate demand, manage controlled loads, and optimize the management of the renewable units installed in these buildings, thereby reaching virtually zero energy use.
Electrical fraud detection: Electricity and gas providers can discover abnormalities or fraud in their use and their customers' installations when they have access to historical consumption data. This data is supplemented with variables such as the location of the meter, the kind of residence, the type of bill, etc. Once this data has been processed, it is possible to create supervised Machine Learning models to detect the earlier anomalies.
In addition, analyzing these data gives valuable information for identifying abnormal behavior, for instance, in household appliances, by analyzing patterns of consumption peaks, normal times of high and low use, etc.
