Integrative electricity need prediction utilizing enhanced squeeze net and flexible Coyote optimization algorithm A combination of RCPs SSPs and socioeconomic
Abstract
Rising global temperatures and frequent extreme heat waves have caused changes in people's energy consumption patterns, leading to a significant impact on electricity consumption. Accurate forecasts of electricity consumption can provide scientific support for the enhancement of power supply services. Precise prediction of power consumption is critical for effective resource management. This research has demonstrated a new approach to forecasting that combines social and economic variables with climate factors using an advanced meta-heuristic algorithm to improve the accuracy and efficiency of the compact network. The effectiveness of the compact network is increased by using the Flexible Coyote Optimization Algorithm (FCOA), which plays a vital role in optimizing parameters and accelerating convergence. This study used the predictive capabilities of common socio-economic pathways (SSP) and representative concentration pathways (RCPs) to simulate influencing factors. Squeeze Net carefully learns complex data patterns to produce reliable predictions, while FCOA focuses on improving the prediction framework. This study has culminated in the creation of a comprehensive forecasting model for electricity demand. Through rigorous testing and comparative analysis, this model has been proven to outperform traditional forecasting methods in terms of accuracy and efficiency.
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