Abstract
The thirst of the Earth for energy is lurching towards catastrophe in an era of increasing water shortage where most of the power plants are hydroelectric. The hydro-based power systems are facing challenges in determining day-ahead generation schedules of cascaded hydropower plants. The objective of the current study is to find a speedy and practical method for predicting and classifying the future schedules of hydropower plants in order to increase the overall efficiency of energy by utilizing the water of cascaded hydropower plants. This study is significant for water resource planners in the planning and management of reservoirs for generating energy. The proposed method consists of data mining techniques and approaches. The energy production relationship is first determined for upstream and downstream hydropower plants by using multiple linear regression. Then, a cluster analysis is used to find typical generation curves with the help of historical data. The decision tree algorithm C4.5, Iterative Dichotomiser 3-IV, improved C4.5 and Chi-Squared Automatic Interaction Detection are adopted to quickly predict generation schedules, and detailed comparison among different algorithms are made. The decision tree algorithms are solved using SIPINA software. Results show that the C4.5 algorithm is more feasible for rapidly generating the schedules of cascaded hydropower plants. This decision tree algorithm is helpful for the researchers to make fast decisions in order to enhance the energy production of cascaded hydropower plants. The major elements of this paper are challenges and solution of head sensitive hydropower plants, using the decision-making algorithms for producing the generation schedules, and comparing the generation from the proposed method with actual energy production.
Highlights
Among other renewable energy resources, such as wind, solar, etc., hydropower is the vital source of producing electricity around the globe
The quick generation values as scaling of data is not required in decision tree algorithm limitation of this work is that small fluctuation in data may alter the feasible decision limitation of this work is that small fluctuation in data may alter the feasible decisio tree structure which is not suitable for long-term hydro scheduling because of the large structure which is not suitable for long-term hydro scheduling because of the large number of variables
Hydropower is considered as a clean source of renewable energy, and, in order to generate more energy, the data mining algorithms and techniques such as clustering, regression and decision tree algorithms are used to explore more valuable information from the available data of the power system
Summary
Among other renewable energy resources, such as wind, solar, etc., hydropower is the vital source of producing electricity around the globe. It reduces the emission of greenhouse gases which is one of the main aspects of global warming. Precipitation, melting snow and streams are the main supply of inflow for hydropower plants. For short-term hydro scheduling, inflow forecasting is used to identify predictions while stream flow forecasting is helpful for calculating the long-term hydrological cycle, such as evaporation, temperature and precipitation. The power generation from a hydropower reservoir depends on different parameters, such as yearly discharge, water head, load rate, etc. Good planning and slight computational improvement can give more energy with the same quantity of water [8]
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