Exploratory Analysis and Pattern Recognition in Energy Production and Demand: A Data-Driven Approach Using Multi-Source Energy Metrics
Hydropower is a leading renewable energy source due to its high efficiency and low operational costs. However, it still faces significant environmental, operational, and forecasting challenges. This paper explores the use of machine learning (ML) models such as SARIMA, Random Forest (RF), and Neural Basis Expansion Analysis for Time Series (NBEATS) to optimize hydropower operations. By analyzing diverse data sets, including hydrometeorological data, plant operations, sensor inputs, and other energy production and demand metrics such as solar, wind, coal, nuclear, and storage, ML enhances decision-making in areas such as inflow forecasting, predictive maintenance, and environmental sustainability. The paper presents an exploratory analysis of 48 -hour energy production and demand patterns across multiple sources (Hydro, Coal, Solar, Wind, Nuclear, and Storage), offering insights into interdependencies and system behavior. It also reviews current ML applications in hydropower, highlights challenges such as data quality and model interpretability, and discusses emerging technologies such as reinforcement learning, explainable AI (XAI), and digital twins as promising future directions. 2025 IEEE.
Tiwari, Akshra; Dass, Teresa Mahima; Tiwari, Vibha; Alapatt, Bosco Paul
2025 International Conference on Engineering Innovations and Technologies, ICoEIT 2025;pp.1418-1423
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICoEIT63558.2025.11211558 https://www.scopus.com/pages/publications/105025045981?origin=resultslist
Tiwari A., School of Sciences, Christ University, Delhi, India; Dass T.M., School of Sciences, Christ University, Delhi, India; Tiwari V., Centre for Artificial Intelligence, Madhav Inst. of Tech & Science, Gwalior, India; Alapatt B.P., School of Sciences, Christ University, Delhi, India