Optimizing Energy Efficiency in Wood Processing Plants through Data Analysis: A Case Study on Value-Added Wood Products Manufacturing

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dc.contributor.authorİnce, Melike Nur
dc.contributor.authorTaşdemir, Çağatay
dc.date.accessioned2026-02-08T15:03:21Z
dc.date.available2026-02-08T15:03:21Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractEnergy consumption in value-added wood products manufacturing facilities has significant environmental and economic impacts. High energy usage increases costs and expands the carbon footprint, making it challenging to achieve sustainability goals. Inefficient energy management in wood processing plants elevates operational costs and exacerbates the environmental burden. Therefore, optimizing energy efficiency through data analysis techniques is critically important. This study analyzes energy consumption data to identify inefficiencies and propose effective optimization strategies. Historical data encompassing operational parameters, energy consumption, environmental conditions, and production output from five high-capacity wood processing machines in the wood products industry were collected daily over the past three years. The dataset includes ten categories: Date, Machine ID, Runtime Hours, Load Percentage, Electricity Usage, Gas Usage, Temperature, Humidity, Production Output, and Energy Efficiency. Initially, the data were loaded into a pandas Data Frame, converted to date and time format, and checked for missing and outlier values, followed by standardization of numerical features. Descriptive statistics were calculated for each feature, and data consistency was verified. The distributions of critical features were visualized with histograms, and the relationships between numerical features were illustrated using a correlation matrix heatmap. Trends and seasonal patterns in energy consumption and production output were analyzed by resampling the data monthly. Principal Component Analysis (PCA) was applied to reduce the dimensionality of the dataset while retaining significant information, and three clusters were formed using the K-Means algorithm. The clusters were visualized in the PCA-reduced feature space, and their characteristics were analyzed to prioritize machines for energy efficiency improvements. Cluster 2, characterized by an average energy usage of 209.79 kWh, an average gas usage of 107.90 m³, an average production output of 1595.03 units, an average energy efficiency of 5.26 units/kWh, and an average load percentage of 75.35%, demonstrated low energy consumption and high production output, indicating highly efficient operations. Therefore, it is recommended that the best practices from this cluster be standardized and implemented across other clusters. Additionally, investing in technological advancements to enhance energy efficiency and conducting continuous improvement efforts to maintain and improve efficiency are suggested.
dc.identifier.endpage63
dc.identifier.issn3062-3014
dc.identifier.issue1
dc.identifier.startpage48
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4055
dc.identifier.volume7
dc.language.isoen
dc.publisherSakarya University of Applied Sciences
dc.relation.ispartofInternational Journal of Data Science and Applications
dc.relation.ispartofInternational Journal of Data Science and Applications
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20260207
dc.subjectData Analysis
dc.subjectVeri Analizi
dc.titleOptimizing Energy Efficiency in Wood Processing Plants through Data Analysis: A Case Study on Value-Added Wood Products Manufacturing
dc.typeArticle

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