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Energy Consumption Prediction and Anomaly Detection for Boiler Feed Pump in Power Plant Using Machine Learning and Deep Learning
(2025-04-01)
Khamfoy, Polawut
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Klomwises, Yuwadee
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Srianomai, Sakuna
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.
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Exploring the i3DVAE-LSTM Framework for Generating Exceptionally Rare Anomaly Signals
(2023-01-01)
Kaewkiriya, Thongchai
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Woraratpanya, Kuntpong
In the era of data-driven approaches, ensuring data quality is crucial for developing effective machine learning and deep learning models. While data augmentation is commonly used to increase the sample size, it does not guarantee data quality. Data generation goes beyond augmentation by incorporating additional steps to ensure high-quality output samples. This technique is particularly valuable for anomaly classification tasks with limited training samples. A recent study introduced a 3DVAE-LSTM (3-Dimensional Variational Autoencoders-Long Short-Term Memory) approach for generating extremely rare case signals. Although this framework synthesized samples for training deep learning models, it faced challenges with long sequential data. To address this, the authors proposed an improved version called i3DVAE-LSTM (Improvement of 3-Dimensional Variational Autoencoders-Long Short-Term Memory) and presented the evaluation of the i3DVAE-LSTM framework. The proposed framework adopts a divide-and-conquer technique, splitting long sequence data into smaller fragments to enhance the quality of generated samples, which are then concatenated together. Experimental results demonstrated that classification models trained with data generated by i3DVAE-LSTM outperformed baselines in all aspects.
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3DVAE-LSTM for Extremely Rare Anomaly Signal Generation
(2022-01-01)
Kaewkiriya, Thongchai
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Woraratpanya, Kuntpong
To overcome the uncontrolled quality output problem of data augmentation, many data generation frameworks have been proposed recently. The main concept of the data generation is for ensuring the quality of the output samples which maintain the original characteristics and highly provide the diversity of data. The benefit of this concept is improving the performance of deep learning tasks that suffer from the lack of available training samples, such as anomaly classification. Recently, 3D variational autoencoder for extremely rare case signal generation (3DVAE-ERSG) was introduced. This framework achieves the best synthesis samples for multi-class classification deep learning training. However, it is not so well applicable to sequential data. Therefore, this paper proposed a 3DVAE-LSTM framework. The new framework was replaced a VAE's feed-forward neural network with a long short-term memory (LSTM) neural network that works well with time-series signals. The experimental results show that the classification models trained with data generated by 3DVAE-LSTM have better performance than 3DVAE-ERSG in every aspect.
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Dependable Sensing System for Pig Farming
(2019-11-01)
Ariyadech, Sripong
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Bonde, Amelie
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Sangpetch, Orathai
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Woramontri, Woranun
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Siripaktanakon, Wachirawich
We have deployed our smart sensing system in a real commercial farm complex for at least 17 months. One of critical factors to success of the sensing system deployment is the resilience and fault tolerance of the system in a harsh environment with unreliable infrastructure and limited access. Power interruptions and intermittent connectivity is not uncommon. Sensors must work even submerging in animal excretion. Correct and continuous streams of sensor data is essential to our smart farming analytics. To make our sensing system sustain such challenges, we have designed and implemented the system with a capability of self-rejuvenation to ensure system liveness. We also equip it with our anomaly detection system to examine small sensor connectivity logs in order to identify potential faulty or deteriorating sensors or external event abnormality with minimal manual intervention.
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Web attack detection using chromatography-like entropy analysis
(2015-01-01)
Watcharapupong, Akkradach
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Threepak, Thanunchai
Web services are mostly attacked in various ways directly and indirectly. We calculate the Shannon entropy from web server log files, especially access logs, and then estimate the entropy distance to detect intrusions and identified them by distinct attack word lists as general, cross-site script, and SQL injection attacks. The experiment shows that our proposed chromatography-like entropy analysis method can detect and identify these behaviors.
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Web attack detection using entropy-based analysis
(2014-01-01)
Threepak, T.
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Watcharapupong, A.
Web attacks are increases both magnitude and complexity. In this paper, we try to use the Shannon entropy analysis to detect these attacks. Our approach examines web access logging text using the principle that web attacking scripts usually have more sophisticated request patterns than legitimate ones. Risk level of attacking incidents are indicated by the average (AVG) and standard deviation (SD) of each entropy period, i.e., Alpha and Beta lines which are equal to AVG-SD and AVG-2*SD, respectively. They represent boundaries in detection scheme. As the result, our technique is not only used as high accurate procedure to investigate web request anomaly behaviors, but also useful to prune huge application access log files and focus on potential intrusive events. The experiments show that our proposed process can detect anomaly requests in web application system with proper effectiveness and low false alarm rate. © 2014 IEEE.
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Anomaly SQL SELECT-statement detection using entropy analysis
(2014-01-01)
Threepak, Thanunchai
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Watcharapupong, Akkradach
Database systems are often intruded because they store valuable information and can be accessed through Internet web applications which sometimes are not developed with security in mind. Attackers can inject some crafted inputs to those programs that work on database systems so that some unexpected results occur. We analyze the database system log files, focus on query statements (SQL SELECT statements), using the Shannon entropy to detect such anomaly attempts that would change conditional entropy significantly. Our experiment shows that the proposed anomaly detection using entropy analysis is effective. © 2014 Springer International Publishing Switzerland.

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