# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 4 Discussion  
Three machine learning methods were used in the research presented: Elastic Net, Decision Trees, and Neural Network to model energy recovery from waste. The input data used were indicators related to different aspects of energy management, economics, and environmental protection: Electricity Prices by Type of User, Energy Productivity, Final Energy Consumption, Gross Domestic Product at Market Prices, Recycling Rate of Municipal Waste, Household Material Consumption per Capita, Environmental Tax Revenues, and Share of Energy from Renewable Sources. These data are used to forecast energy recovery because, taken together, they provide a comprehensive picture of both energy demand and supply, the effectiveness of its use, and the impact of environmental and economic policies on the energy sector. Analyzing these indicators provides a better understanding of how different factors affect the opportunities for energy recovery and identifies the trends that may affect future energy recovery needs and opportunities. Such a comprehensive approach is necessary to produce accurate and useful forecasts in a dynamically changing energy environment. The research conducted indicates that the ANN model is more effective and accurate in predicting energy recovery from waste compared to the Elastic Net and Decision Trees methods. The calculation and analysis of the quality indicators of the machine learning models, such as R, MSE, RMSE, RIE, and MAE, allowed them to be evaluated in terms of accuracy, stability, and ability to generalize. High R values indicate a strong correlation between predictions and actual values, while high MSE and MAE values indicate less accurate predictions. The neural network model obtained the lowest MSE (242.09) and MAE (11.1631) and the highest R value (0.9911); it managed better with the input data. Decision trees, despite being a simpler model, also showed high efficiency: R = 0.9905, MSE = 253.57, and MAE = 12.0148, suggesting that the data structure fits well with regression-based models. ElasticNet, being a linear method, does not cope with the dependencies of the input data, which is manifested by higher errors (MSE = 2372.8; MAE = 36.97). When building ANN models, the choice of the number of layers, neurons, and activation functions offers much greater flexibility in adapting the model, making it a preferred choice for building models. The disadvantage of ANN models is the difficulty in interpreting the results obtained compared to ElasticNet and Decision Trees. There are few models for forecasting energy recovery from waste based on economic, environmental or social indicators. [PERSON] et al. (2018) developed a General Regression Neural Network model to forecast the annual production of primary energy from fixed municipal waste in European countries [39]. As input data they used the following: Human development index, Gross domestic product, Domestic material consumption, Urban population share, MSW recycling rate, MSW generated, Energy taxes, Share of renewable energy in gross final energy consumption, Energy productivity, Number of main electric retailers per million inhabitants, Final energy consumption, Electricity prices by medium-size industry, and Electricity prices by medium-size households. The model was characterized by the determination factor R\({}^{2}\) = 0.995 and the mean absolute percentage error MAPE = 7.757% [39]. [PERSON] et al. (2021) developed an artificial neural network model to predict the amount of waste in Poland [40]. As input data, the following were used: Population, Income per capita, Employment to population ratio, Number of enterprises registered in the region per 10,000 inhabitants, and Number of enterprises by type of business activity. The model yielded R = 0.98 [40]. Comparative experiments are an important element in assessing the effectiveness of machine learning models. Much of the current research is based on a single machine learning model without considering other machine learning models [8; 41].