###### Abstract  
The increasing importance of hydropower generation has led to the development of new smart technologies and the need for reliable and efficient equipment in this field. As long as hydropower plants are more complex to build up than other power plants, the operation regimes and maintenance activities become essential for the hydropower companies to optimize their performance, such that including the data-driven approaches in the decision-making process represents a challenge. In this paper, a comprehensive and multi-task framework integrated into a Knowledge Discovery module based on Data Mining to support the decisions of the operators from the control rooms and facilitate the transition from the classical to smart Supervisory Control and Data Acquisition (SCADA) system in hydropower plants has been designed, developed, and tested. It integrates tasks related to detecting the outliers through advanced statistical procedures, identifying the operating regimes through the patterns associated with typical operating profiles, and developing strategies for loading the generation units that consider the number of operating hours and minimize the water amount used to satisfy the power required by the system. The proposed framework has been tested using the SCADA system's database of a hydropower plant belonging to the Romanian HydroPower Company. The framework can offer the operators from the control room comparative information for a time horizon longer than one year. The tests demonstrated the utility of a Knowledge Discovery module to ensure the transition toward smart SCADA systems that will help the decision-makers improve the management of the hydropower plants. s +
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On the other hand, efficient planning and computational enhancements can increase the energy output by using the same available water [2]. The optimal operation of a hydroelectric power plant involves the gathering and processing of vast amounts of input data. Unfortunately, the techniques used in the design and implementation of the plant's operations are not always able to extract the most value from the data. This paper proposes a clustering-based method that can help the Decision-Makers (DEMA) identify the optimal hourly load patterns of the generators. The daily generation scheduling method is an integral part of the decision-making process in a power plant. It helps reduce the time it takes to make critical decisions by implementing effective maintenance plans and energy production techniques [5]. The increasing importance of hydropower generation has led to the development of new technologies and the need for reliable and efficient equipment. It has become a vital factor in the operations and maintenance of these machines. Hydropower plants are typically more expensive to set up than other energy sources. Nonetheless, they have a longer lifespan than other power plants. The lifespan of a hydropower plant can be up to 50 years or more, which is longer than that of thermal plants. Usually, the economic and financial analyses suppose a lifespan of 30-40 years [6]. The operations and maintenance (O&M) costs should not be overlooked by the DEMA. They are typically around 2% of the investment. The specific O&M cost for large projects is around 2% to 2.5% and for small projects from 1% to 6% [7]. Although routine maintenance is carried out on the equipment, digital solutions can improve predictive maintenance allowing the hydropower plants to increase their efficiency [8], helping to maximize the life of a plant's resources and assets. A study presented in [9] concluded that digitalization could improve the efficiency of a hydropower plant by 1% by better distributing the flow among the different turbine units, and the annual energy production can be increased by approximately 11%, depending on the site, if the spills and the hours to manage manual operations are reduced. It can also prevent costly repairs by identifying potential issues early.