Srianomai, Sakuna
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Item type:Publication, Energy Consumption Prediction and Anomaly Detection for Boiler Feed Pump in Power Plant Using Machine Learning and Deep Learning(2025-04-01); Enhancing energy efficiency and operational reliability is crucial in power plant management, particularly for high-energy-consuming machines such as boiler feed water pumps (BFPs). These pumps play a vital role in the continuous generation of steam and electricity and must operate 24/7 to maintain power production stability. This study proposes the development of predictive models based on machine learning and deep learning techniques to accurately predict energy consumption and applies best models to detect anomalous behaviors in BFPs, enabling timely and preventive interventions. A dataset comprising 43,082 hourly records over five years, with 18 critical operational features, was analyzed using preprocessing and feature engineering techniques. Various predictive models were trained and evaluated, including Multiple Linear Regression, Regularized Regressions (Ridge, Lasso, ElasticNet), Support Vector Regression (SVR), Decision Tree, Ensemble Methods (Random Forest, XGBoost, CatBoost, LightGBM), and Deep Learning Architectures (DNN, RNN, GRU, LSTM). Among these models, SVR demonstrated the highest accuracy (MSE: 13.5573, R²: 0.9838), followed closely by LightGBM. Feature importance analysis revealed that boiler feed pump discharge pressure and bearing housing vibration levels were the most influential variables in energy consumption prediction. Anomaly detection using the Interquartile Range (IQR) method classified deviations into two warning levels, enabling proactive maintenance strategies. Additionally, a Graphical User Interface (GUI) web application was developed for real-time monitoring, integrating predictive models, anomaly detection, and an automated email alert system to assist operators in responding to abnormal energy consumption events promptly. These results highlight the potential of predictive analytics and real-time monitoring in optimizing power plant operations, providing a foundation for extending predictive capabilities to other critical energy-intensive systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Gamification Approach to Information Visualization and Strategy Discovery - A Case of Thai Hotel Entrepreneur(2022-07-07); Savetpanuvong, PhannaphatrA rise in digital technology advancement has posed an abundance of information in the business world. Traditional reporting in small and medium enterprises is no longer adequate for making the right and timely decisions in strategic and operational management. Novel Business Intelligence (BI) tools provide an opportunity for business users to perform data analytics with dashboard multidimensional visualization. Data on 26,051 records in 2019 from a hotel business was acquired for this research. Business insights, which had not been discovered before by traditional reporting, were revealed regarding occupancy driving factors, customer behavior, accommodation preference, and channel efficiency. Two dashboards were developed with gamification design principles that allow business users to engage with hedonism during the data play for both strategic and tactical levels. Insights shed the light on users concerning actionable items on better campaign offering, hotel resource allocation optimization, and channel dependency balancing.