Abstract
This research examines the impact of temperature, relative humidity, and wind speed on the electricity demand. It presents a unique method that combines an Enhanced Inception-V4 model with an Improved Osprey Optimizer to analyze weather-related factors. The combined model, which has been validated from 2003 to 2023, surpasses traditional forecasting techniques and significantly improves prediction accuracy. The Enhanced Inception-V4 model's ability to process data allows it to identify key factors that influence electricity demand patterns. Meanwhile, the Modified Osprey Optimizer fine-tunes the model's parameters, ensuring its adaptability to different weather scenarios. The study confirms the reliability of the OOI-Inception-V4 model in forecasting electricity demand and highlights the strong connection between weather conditions and energy usage, especially during extreme weather events. The projected increase in electricity demand from 2024 to 2030 emphasizes the importance of proactive energy policies, infrastructure upgrades, and sustainability initiatives. The research underscores the crucial role of temperature in driving electricity demand, with noticeable variations during winter and summer due to heightened usage of heating and cooling systems. In general, this study emphasizes the significant impact of climate on energy demand and demonstrates the potential of advanced predictive models in enhancing electricity demand forecasting.