Forecasting U.S. Fossil Energy Consumption: Advancing Accuracy with Multilayer Perceptron and Residual Learning

Accurate midterm forecasts of fossil fuel consumption are essential for effective energy planning, economic management, and resource allocation. While machine learning models have demonstrated their efficacy in handling large-scale nonlinear datasets, many, including Multilayer Perceptrons (MLPs), suffer from performance degradation with increased depth. Fortunately, recent studies have revealed that Residual Networks (ResNets) can mitigate or even overcome this challenge. In this paper, we propose a Weighted Residual Network based on MLP to enhance predictive performance. We employ the Adam algorithm for model training and utilize the Gridsearch algorithm for hyperparameter tuning. In the application section, we develop predictive models using three case studies: natural gas, petroleum, and total fossil fuel consumption. We validate the effectiveness of our proposed model and compare it with ten other machine learning models. Our findings demonstrate that our proposed model consistently outperforms others in all three cases, underscoring its superior performance in midterm forecasting of fossil fuel consumption.