Predictive Analytics for Energy Forecasting and Optimization

The increasing electricity demand, combined with the continuous depletion of fossil fuel reserves, has intensified the search for alternative renewable energy sources. To bridge the gap between energy demand and supply, the development of an affordable electrical infrastructure is imperative. Such an infrastructure should encourage energy conservation and discourage using energy-intensive devices during peak hours. Achieving these goals relies heavily on the accuracy and time complexity of algorithms used for electricity consumption prediction. Additionally, factors such as weather conditions and static characteristics must be taken into account, as they significantly influence user acceptance and technology adoption. Recent research has shown that integrating energy-saving knowledge and environmental awareness into the second generation of the UTAUT model can positively impact the acceptance of smart meters among residential consumers. Moreover, the implementation of Demand Response (DR) programs can effectively reduce system peaks and generate billions of dollars in annual savings. Notably, studies have indicated that a small percentage of customers (20%) account for a substantial majority (80%) of price responses, emphasizing the potential impact of a targeted approach. Researchers have demonstrated the effectiveness of such strategies by training a model on data from over 5567 households and validating it with another algorithm. The implementation of a smart electrical grid holds promise in meeting the increasing demand for electricity, promoting energy conservation, and mitigating peak-hour energy usage. The success of this endeavor relies on the deployment of accurate and efficient algorithms, the widespread acceptance of smart technologies, and the utilization of big data analytics. By embracing these factors, we can pave the way toward a sustainable and efficient energy future.