  The scheduling and operation of power system becomes prominently complex and
uncertain, especially with the penetration of distributed power. Load
forecasting matters to the effective operation of power system. This paper
proposes a novel deep learning framework to forecast the short-term grid load.
First, the load data is processed by Box-Cox transformation, and two parameters
(electricity price and temperature) are investigated. Then, to quantify the
tail-dependence of power load on the two parameters, parametric Copula models
are fitted and the threshold of peak load are computed. Next, a deep belief
network is built to forecast the hourly load of the power grid. One year grid
load data collected from an urbanized area in Texas, United States is utilized
in the case studies. Short-term load forecasting are examined in four seasons
independently. Day-ahead and week-ahead load forecasting experiments are
conducted in each season using the proposed framework. The proposed framework
is compared with classical neural networks, support vector regression machine,
extreme learning machine, and classical deep belief networks. The load
forecasting performances are assessed by mean absolute percentage error, root
mean square error, and hit rate. Computational results confirm the
effectiveness of the proposed data-driven deep learning framework. The
prediction accuracies of both day-ahead forecasting and week-ahead forecasting
demonstrate that the proposed framework outperforms the tested algorithms.
