# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 1 Introduction  
Energy is a fundamental element for the functioning of contemporary society, and the laws governing its use have both economic and strategic-political implications. The objective of the European Union's energy policy is to decrease reliance on imported energy and energy products by encouraging the implementation of energy-saving measures and the inclusion of renewable energy sources (RES) [1]. Acquiring energy relies on various sources that vary by country. In 2021, renewable energy was the primary source of energy production in the European Union, accounting for 41% of total production. Solid fuels (18%), natural gas (6%), and oil (3%) also contributed to energy production [2]. Energy production in EU countries is highly diverse. In Poland, Estonia, and the Czech Republic, solid fuels are the primary source of energy production accounting for 70.7%, 50%, and 53.60% of total electricity production, respectively [3]. However, Norway and Sweden have a significant share of renewable energy, with 73% and 55%, respectively. Norway is renowned for its use of hydropower, which accounted for 81% of renewable energy sources in 2018. In contrast, Sweden is known for its production of wind energy, which accounted for 20%. Finland utilizes biomass for both heating and power generation, with 79% of its renewable energy coming from this source [4]. Global energy consumption has been increasing annually by an average of 1% to 2% for over half a century. EuropeanUnion (EU) countries are seeking innovative and sustainable solutions to improve waste management and energy efficiency in response to the growing demand for energy and the ongoing challenge of municipal waste. One promising direction is the conversion of municipal waste to energy. This approach reduces the amount of waste reaching landfills and produces energy. It objectively assesses the potential benefits of this approach [5]. The conversion of municipal waste to energy, known as Waste-to-Energy (WTE), not only aids in waste management but also contributes to the production of renewable energy and reduces greenhouse gas emissions by replacing solid fuels. WTE encompasses combustion, pyrolysis, gasification, and methane fermentation. It enables the production of energy from municipal waste that would otherwise be sent to landfills, thus diversifying energy sources and increasing energy security [6]. The conversion of municipal waste to energy has several advantages. However, this process also presents various challenges and issues. Technological advancements are closely linked to the environmental and economic policies of individual countries and global trends in sustainable development [7]. The variable composition and calorific value of municipal waste pose significant challenges for converting waste into energy [8]. Installations must be designed in a way that enables them to cope with the volatility of the composition and fuel value of waste. This requires the use of advanced processing technologies, such as waste-free systems and exhaust gas purification technology, to minimize pollution emissions [9]. To enhance energy and economic efficiency, waste-to-energy plants can implement several strategies. These include accurate waste segregation at the source stage, pre-processing (such as drying to reduce waste moisture and increase calorific value), and mixing waste streams of varying fuel value to achieve a more stable and optimal combustion mixture [10]. Constructing and maintaining energy processing plants, particularly waste-to-energy facilities, presents a significant challenge due to the substantial investments required. Availability of these facilities can help reduce the complexity of technology and high maintenance costs, particularly in regions with limited financial resources. Therefore, decision-making regarding the implementation of such solutions requires careful assessment of profitability as well as consideration of all arguments for and against.