Artificial intelligence (AI) will have a profound role in climate management actions in Kenya.
By analysing demand and consumption patterns indicative of stability or instability within the electricity system, for instance, AI as a tool has the ability to detect the likelihood of network failure or intrusion by providing network security, thus averting large-scale power outages and grid collapse.
Kenya has in the recent past registered growing incidences of power blackouts that have had significant disruptions to businesses and houses holds, with some regions taking as much as 24 hours to have normalcy restored in power supply.
Resilience modelling provides operators of utility companies with the necessary information to make appropriate decisions.
Secondly, the subject of machine learning and renewable energy integration is one that has gained traction.One critical challenge that continues to present it self in the renewable energy space is the variability of the weather conditions.
By integrating machine learning-driven weather forecasting into renewable energy system, we can better anticipate fluctuations in energy production. This allows for more effective storage planning and grid management.
Just recently KenGen announced its intention to invest in Battery Energy Storage System (BESS).
Integration of AI generated algorithms to BESS technology would help analyse consumption patterns and determine the most efficient moment to charge and discharge from batteries storage, which ensures renewable energy particularly wind and solar are available when needed.
This also guarantees better grid stability and thus helps address the optimality question.
The third aspect relates to hydro-power technology. In Kenya this happens to be the oldest technology for power generation. One advantage that AI brings into hydro power is predictive maintenance.
Machine learning algorithms can better analyse data from sensors and equipment and help predict potential issues before they become critical in our hydro-power plants.
This proactive approach minimises downtime, reduces maintenance costs, and ensures the longevity of hydro-electricity infrastructure.
Finally, the reduction of the carbon footprint, through enhanced energy efficiency, in sectors where machine language is integrated, contributes positively to environmental sustainability. In-addition to rolling out anticipatory measures before any adverse eventuality occurs.
