Energy transition modeling is crucial for controlling the transition from non-renewable to renewable energy sources. The transition requires accurate predictions of future demand and production capacities for infrastructure planning and policy formulation. This paper aims to demonstrate a method involving data processing on input consumption data and deploying machine learning models to forecast future demand based on consumption patterns and weather features. The paper shows a data processing methodology that serves as a framework for energy prediction tasks. A novel Seq2Seq encoder–decoder architecture utilizing LSTM unit cells is proposed. The proposed method provides more accurate results by comparing the proposed Seq2Seq LSTM model with conventional forecasting techniques. It thus can potentially be used to predict the forecasted load with lesser error, as shown through two publicly available datasheets. The proposed model can act as a valuable framework for energy demand forecasting and handle complex patterns and diverse influencing factors such as occupancy rates and meteorological factors.