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Accurate load forecasting is critical for power system planning and operational decision making. In this study, we are the first to utilize a deep feedforward network for short-term electricity load forecasting. Our results are compared to those of popular machine learning models such as random forest and gradient boosting machine models. Then, electricity consumption patterns are explored based on...
Highly accurate short-term power load forecasting (STLF) is fundamental to the success of reducing the risk when making power system planning and operational decisions. For quantifying uncertainty associated with power load and obtaining more information of future load, a probability density forecasting method based on quantile regression neural network using triangle kernel function (QRNNT) is proposed...
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