Predicting Solar-Harvested Energy for Resource-Constrained IoT Devices Using Machine Learning
2024 20TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SMART SYSTEMS AND THE INTERNET OF THINGS, DCOSS-IOT 2024（2024）
Key words
Internet Of Things,Internet Of Things Devices,Light Intensity,Random Forest,Machine Learning Models,Mean Absolute Error,Photodetector,Multilayer Perceptron,Ambient Light,Gradient Boosting,Quantile Regression,Mean Absolute Percentage Error,Ambient Measurements,Random Gradient,30-minute Intervals,Time Interval,Neural Network,Learning Algorithms,Random Forest Model,Energy Harvesting,Solar Panels,Gradient Boosting Model,Tree-based Machine Learning,Multilayer Perceptron Model,Internet Of Things Systems,SHapley Additive exPlanations,Cloudy Days,Energy Prediction,LightGBM,Code Size
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