# ISO 50001 Data Driven Methods for Energy Efficiency Analysis of Thermal Power Plants
###### Abstract
In recent years, the application of this standard has been tested at different levels both from an industrial and a scientific point of view. In [8], the authors have assessed the practical implementation of the ISO 50001 standard in the industrial context, analyzing the application in three Chinese industries. Additional studies have been carried out investigating the application of the standard in municipalities [9]. The EMS is not a static tool, but it evolves and dynamically improves throughout the time. In order to implement the dynamical approach, we can apply the PDCA cycle to the plant EMS. In the green energy transition and renewable energy sources intermittency context, Combined Cycle Power Plants (CCPP) still represent a great potential in terms of higher efficiency, availability, reduced environmental pollution and smaller site space [10]. In Figure 1, a layout of a classical combined power plant with one gas turbine and a steam turbine is sketched. This plant is a well-recognized technology based on the transformation of gas fossil fuels in electrical energy. The process is based on one or more gas turbines working in parallel and a steam turbine alimented by the waste heat of the gas combustion process. The extra power generated leads to a production that is 50% greater with respect to a traditional simple cycle, with an overall efficiency up to 60% [11]. Proper power plant modeling is a significant aspect of power plant operation optimization, cost reduction and energy efficiency [12]. Data driven modeling has been addressed in the recent past in order to consider also power plant monitoring and risk management [13]. In this framework, model-based methods use numerical surrogate models for the system under control for fault diagnosis and maintenance purposes [14]. In recent years, several Artificial Intelligence (AI) and machine learning techniques have been implemented in order to predict and monitor power production [15], power consumption [16] and optimized energy management and power control systems [17]. Among these techniques, several neural network approaches [18] have been adopted as well as deep learning techniques [19] and probabilistic approaches [20]. Alternatively, energy savings can be improved by using population-based multi-optimization techniques, as shown in [21, 22]. The original target of this work, from a broad perspective, was to introduce novel artificial intelligence techniques in a common thermal power plant owned by a big energy company, where traditionally energy savings, sustainability and environmental impacts were not taken into account as a priority. In the context of a real combined cycle power plant, in order to enhance the digital technologies to be implemented in a structured multi-level energy analysis, we develop an AI-based tool with the aim of improving the existing industrial procedures and overtaking their obsolete in-field operation for each plant. More specifically, the main novel contributions of this work are on the one hand to develop an AI-based energy model to improve predictive capability with respect to traditional thermal consumption units, and on the other hand to perform an energy efficiency analysis using the above-mentioned performance indicators (EnPI and EnB). This manuscript is structured as follows: Section 2 describes a preliminary analysis of the power plant consumption, starting from the ISO 50001 recommendations, to build a proper neural model of the power plant subsystems; in Section 3, a definition of cooling towers' optimal consumptions through a feed-forward artificial neural network is described. Results are presented and commented upon in Section 4, before the conclusions.