The instability of power generation from national grids has led industries (e.g., telecommunication) to rely on plant generators to run their businesses. However, these secondary generators create additional challenges such as fuel leakages in and out of the system and perturbations in the fuel level gauges. Consequently, telecommunication operators have been involved in a constant need for fuel to supply diesel generators. With the increase in fuel prices due to socio-economic factors, excessive fuel consumption and fuel pilferage become a problem, and this affects the smooth run of the network companies. In this work, we compared four machine learning algorithms (i.e. Gradient Boosting, Random Forest, Neural Network, and Lasso) to predict the amount of fuel consumed by a power generation plant. After evaluating the predictive accuracy of these models, the Gradient Boosting model out-perform the other three regressor models with the highest Nash efficiency value of 99.1%.
Forecasting Sensor Values in Waste-To-Fuel Plants: a Case Study
University of Ljubljana（卢布尔雅那大学）；Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Qlector d.o.o.（Qlector有限公司）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）
Constrained Optimal Fuel Consumption of HEVs under Observational Noise
AI Technology Research and Development Department, Automotive New Technology Research Institute, BYD Auto Industry Company Limited（比亚迪汽车工业有限公司智能技术研究与开发部、汽车新技术研究院）；Tsinghua University（清华大学）
Learning Eco-Driving Strategies at Signalized Intersections
MIT（麻省理工学院）；Laboratory for Information & Decision Systems（信息与决策系统实验室）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Civil and Environmental Engineering（土木与环境工程系）；Institute of Data Systems and Society（数据系统与社会研究所）
Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants
Rhodes University（罗德斯大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；Ghana Space Science and Technology Institute（加纳空间科学技术研究所）；Ghana Atomic Energy Commission（加纳原子能委员会）；University of Ngaoundéré（恩冈代雷大学）；University of Ferrara（费拉拉大学）
Constrained Bayesian Optimization for Hierarchical Federated Learning in IoT Networks for Plant Disease Classification
University of Zagreb（萨格勒布大学）；University of Thessaly（塞萨利大学）；International Hellenic University（国际希腊大学）；University of Glasgow（格拉斯哥大学）；Trinity College Dublin（都柏林圣三一学院）；Italian National Research Council（意大利国家研究委员会）
