Sustainable Energy Consumption Forecasting in Smart Grids using Autoregressive Integrated Moving Average Model

With the increase in global energy needs, anticipating sustainable energy use has become essential, especially in smart grids. This research uses the Autoregressive Integrated Moving Average (ARIMA) model to forecast sustainable energy usage in smart networks, addressing demand variability. Data gathering historical energy usage data from smart grids, including parameters such as meteorological circumstances, temporal variables, and user behavior. Utilizing the ARIMA model, examine time series data to discern patterns and seasonal fluctuations in energy use. The model's parameters are refined to improve predictive accuracy, offering significant insights for utility firms. The findings demonstrate that ARIMA proficiently identifies the fundamental trends in energy use, resulting in enhanced resource distribution and minimized energy waste. Results indicate higher predictive precision, facilitating effective energy management and increased system dependability. This forecasting method facilitates the incorporation of renewable energy sources into the system, enhancing sustainability. It illustrates the efficacy of ARIMA in improving decision-making for energy suppliers and facilitating a shift towards more sustainable energy systems in smart grids.