Dynamic Thermal Rating Forecasting Methods: A Systematic Survey

Dynamic Thermal Rating (DTR) allows optimum electric power line rating use. It is an intelligent grid technology predicting changes in due to changing physical and environmental conditions. This study performed a meta-analysis of DTR forecasting methods by classifying the methods, implementing them, comparing their outputs for 24hr forecast lead time. implemented deep learning Recurrent Neural Network (RNN), Ensemble Means Convolution (CNN). RNN uses initial outcome specific neural network layer as feedback predict layer’s outcome. Monte-Carlo simulation process producing random, equally viable solutions. On other hand, CNN unsupervised features with minimal errors. survey systematically implements Quantile Regression (QR), RNN, means forecasting. Point error metrics probabilistic sharpness, skill, bias were used methods’ evaluation. All tested prove be efficient, but 50th percentile QR appears more conservative, secure less error-prone. achieved between 35% - 45% capacity utilization over Static (STR). average, judging all quantile regression proves highly reliable provides better conviction our choice