scholarly journals Modeling and Estimating of Load Demand of Electricity Generated from Hydroelectric Power Plants in Turkey using Machine Learning Methods

2014 ◽  
Vol 14 (1) ◽  
pp. 121-132 ◽  
Author(s):  
B. DURSUN ◽  
F. AYDIN ◽  
M. ZONTUL ◽  
S. SENER
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
Hydroelectric Power ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Load Demand ◽  
Hydroelectric Power Plants

scholarly journals Time series forecasting using ensemble learning methods for emergency prevention in hydroelectric power plants with dam

2022 ◽  
Vol 202 ◽  
pp. 107584
Author(s):  
Stéfano Frizzo Stefenon ◽  
Matheus Henrique Dal Molin Ribeiro ◽  
Ademir Nied ◽  
Kin-Choong Yow ◽  
Viviana Cocco Mariani ◽  
...  
Keyword(s):  
Time Series ◽  
Ensemble Learning ◽  
Power Plants ◽  
Time Series Forecasting ◽  
Hydroelectric Power ◽  
Learning Methods ◽  
Hydroelectric Power Plants

scholarly journals Development of methods for the formation of operation modes of hydropower systems using machine learning

2019 ◽  
Vol 124 ◽  
pp. 05056
Author(s):  
Ayrat Mardikhanov ◽  
Vilen Sharifullin ◽  
A.V. Golenishchev-Kutuzov ◽  
Sh.G. Ziganshin
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
Forecast Model ◽  
Energy System ◽  
Hydroelectric Power ◽  
Machine Learning Methods ◽  
Operation Modes ◽  
Hydropower Plants ◽  
Solution Energy ◽  
Generating Equipment

The paper describes the method for finding a compromise solution during formation of operation modes of hydropower systems (cascade of hydropower plants). The software solution “Energy system of the HPP cascade” (http://hydrocascade.com) was implemented based on the developed methodology. In the existing model, in order to improve the accuracy of forecasting the parameters of the generating equipment of hydroelectric power plants and hydraulic structures, machine learning methods were used. The new forecast model has increased the accuracy of the forecasts by an average of 3.67%.

scholarly journals Spillage Forecast in Hydroelectric Power Plants via Machine Learning

2021 ◽  
Author(s):  
Pedro H. M. Nascimento ◽  
Ramon Abritta ◽  
Frederico F. Panoeiro ◽  
Leonardo De M. Honório ◽  
André L. M. Marcato ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Water Flow ◽  
Power Plants ◽  
Learning Approach ◽  
Hydroelectric Power ◽  
Water Flows ◽  
Machine Learning Approach ◽  
Hydroelectric Power Plants ◽  
Extract Information

Brazilian hydroelectric power plants often use telemetry stations to extract information about the environment. These equipment are usually installed in several strategic spots of rivers that "feed" the reservoir, and are capable of providing important information such as precipitation, river level, and water flow. This paper presents an analysis of Machine Learning applied to the forecasting of spillage occurrences over a set amount of time in a Brazilian power plant. To achieve this goal, telemetry stations' data were utilized together with the plant's operations historical, which provides information about previous spillages, turbines' flows, among others. The Machine Learning approach has shown to be promising in this problem, and the developed model presented the potential to effectively support decisions by helping the operators prepare for significant incoming water flows.

scholarly journals Bootstrap Aggregating Approach to Short-Term Load Forecasting Using Meteorological Parameters for Demand Side Management in The North-Eastern Region of India

2021 ◽  
Author(s):  
Dipu Sarkar ◽  
Taliakum AO ◽  
Sravan Kumar Gunturi
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
Meteorological Parameters ◽  
Load Forecasting ◽  
Data Availability ◽  
Production Costs ◽  
Eastern Region ◽  
Short Term ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used real-time load data from Nagaland's load dispatch centre in India and meteorological parameters of the Nagaland region for data analysis. The synthetic minority over-sampling technique for regression (SMOTE-R) is also employed to avoid data imbalance issues. The experimental results show that the Bagging methods outperform other models with respect to mean squared error and mean absolute percentage error.

Application of machine learning methods for automatic interpretation of open hole logging data

2020 ◽  
pp. 44-47
Author(s):  
M.A. Basyrov ◽  
◽  
A.V. Akinshin ◽  
I.R. Makhmutov ◽  
Yu.D. Kantemirov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automatic Interpretation ◽  
Open Hole
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