  Electricity load forecasting plays an important role in the energy planning
such as generation and distribution. However, the nonlinearity and dynamic
uncertainties in the smart grid environment are the main obstacles in
forecasting accuracy. Deep Neural Network (DNN) is a set of intelligent
computational algorithms that provide a comprehensive solution for modelling a
complicated nonlinear relationship between the input and output through
multiple hidden layers. In this paper, we propose DNN based electricity load
forecasting system to manage the energy consumption in an efficient manner. We
investigate the applicability of two deep neural network architectures
Feed-forward Deep Neural Network (Deep-FNN) and Recurrent Deep Neural Network
(Deep-RNN) to the New York Independent System Operator (NYISO) electricity load
forecasting task. We test our algorithm with various activation functions such
as Sigmoid, Hyperbolic Tangent (tanh) and Rectifier Linear Unit (ReLU). The
performance measurement of two network architectures is compared in terms of
Mean Absolute Percentage Error (MAPE) metric.
