  The prediction of near surface wind speed is becoming increasingly vital for
the operation of electrical energy grids as the capacity of installed wind
power grows. The majority of predictive wind speed modeling has focused on
point-based time-series forecasting. Effectively balancing demand and supply in
the presence of distributed wind turbine electricity generation, however,
requires the prediction of wind fields in space and time. Additionally,
predictions of full wind fields are particularly useful for future power
planning such as the optimization of electricity power supply systems. In this
paper, we propose a composite artificial neural network (ANN) model to predict
the 6-hour and 24-hour ahead average wind speed over a large area (~3.15*106
km2). The ANN model consists of a convolutional input layer, a Long Short-Term
Memory (LSTM) hidden layer, and a transposed convolutional layer as the output
layer. We compare the ANN model with two non-parametric models, a null
persistence model and a mean value model, and find that the ANN model has
substantially smaller error than each of these models. Additionally, the ANN
model also generally performs better than integrated autoregressive moving
average models, which are trained for optimal performance in specific
locations.
