ENDING ENERGY POVERTY VIA ARTIFICIAL INTELLIGENCE FOR DEMAND RESPONSE PROGRAMS ON ENERGY GRID STRESS ALLEVIATION BY INCENTIVES TO CUSTOMERS OFF-PEAK USAGE INCLINATION
Authors: Nyameri Edward Momanyi 
Published: May 2019

As the world becomes more reliant on electricity for everyday activities, such as transportation, communication, and heating or cooling, the stress on energy grids is rapidly increasing. This has led to the development of demand response programs, which incentivize customers to reduce their electricity usage during peak demand periods, thereby alleviating the stress on the grid. To make demand response programs more effective, artificial intelligence (AI) can be employed to better predict energy demand and incentivize customers to participate. AI has the potential to significantly improve the accuracy of demand prediction, which is key to the success of demand response programs. Traditionally, demand response programs have relied on simple statistical analyses to predict energy demand patterns. However, these analyses are often inaccurate, as they fail to take into account a variety of factors, such as weather patterns or individual customer behavior. AI, on the other hand, can analyze vast amounts of data from various sources, such as weather forecasts, energy usage patterns, and customer demographics, to create more accurate predictions of energy demand. Moreover, AI can use this data to create personalized incentives for each customer to participate in demand response programs. Rather than offering the same incentives to all customers, which may not be effective for certain groups, AI can tailor incentives to each individual based on their energy usage patterns, lifestyle, and other relevant factors. For example, an AI algorithm could predict that a particular customer is more likely to reduce their energy usage during a heatwave, and offer that customer a higher incentive to do so. In addition, AI can optimize energy usage across multiple devices and systems, such as electric vehicles or smart homes, to increase the efficiency of demand response programs. By analyzing patterns in energy usage across multiple devices, AI can identify opportunities for energy savings, such as shifting EV charging to non-peak periods, or adjusting smart thermostat settings to reduce cooling during peak demand periods. AI can also reduce the administrative burden of demand response programs, which can be complex and time-consuming to manage. By automating the process of enrolling customers, predicting demand, and offering incentives, AI can streamline demand response programs and make them more efficient. However, there are also several challenges that must be addressed to ensure the successful implementation of AI in demand response programs. One of the main challenges is ensuring the privacy and security of customer data. AI relies on vast amounts of data to create accurate predictions and personalized incentives, but this data must be protected from unauthorized access or misuse. Ensuring that customer data is secure and private is essential to gaining customer trust and participation in demand response programs. Another challenge is ensuring that the incentives offered to customers are cost-effective for utilities and grid operators. AI can offer highly personalized incentives to customers, which may be more effective at encouraging participation, but these incentives must also be financially viable for utilities and grid operators. Balancing these competing objectives is crucial to the success of demand response programs. Despite these challenges, the potential benefits of AI for demand response programs are substantial. By improving demand prediction, optimizing energy usage, and offering personalized incentives, AI can help alleviate the stress on energy grids and reduce the need for expensive energy infrastructure upgrades. As the world moves towards a more sustainable energy future, demand response programs powered by AI will play an increasingly important role in ensuring the stability and reliability of energy grids. In conclusion, artificial intelligence has the potential to significantly improve demand response programs by improving demand prediction, optimizing energy usage, and offering personalized incentives to customers. However, challenges such as privacy and security concerns, and the balancing of cost-effective incentives, must be carefully addressed to ensure the success of these programs. As the world continues to move towards a more sustainable energy future, demand response programs will be or shall prove to be an essential tool Citations: - Faruqui, A., Harris, D., Hledik, R., &amp; Kihm, S. (2010). Unlocking the full potential of demand response.