As the global energy landscape evolves towards sustainability, the extensive usage of fossil fuels in electricity generation is progressively diminishing, while the contribution of renewable energy sources is steadily increasing. In this evolving scenario,importance of load forecasting cannot be overstated in optimising energy management and ensuring the efficient operation of industrial plants regardless of their scale. By accurately anticipating energy demand, industral facilities can enhance efficiency, reduce costs, and facilitate the adoption of renewable energy technologies in the power grid. Recent studies have emphasised the pervasive utilisation of machine learning-based algorithms in the field of electric load forecasting for industrial plants. Their capacity to analyse intricate patterns and enhance prediction accuracy renders them a favoured option for enhancing energy management and operational efficiency. The present analysis revolves around the creation of short-term electric load forecasting models for a large industrial plant operating in Adana, Türkiye. The integration of calendar, meteorological, and lagging electrical variables, along with machine learning-based algorithms, is employed to boost forecasting accuracy and optimize energy utilization.The ultimate objective of the present study is to conduct a thoroughgoing and detailed analysis of statistical performance and associated error metrics. The metrics to be employed include the R², MAE, RMSE, and MAPE.