A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2. Energies 2019, 12, 3359 2 of 16users' load demand. The major concern of this scheme is the cost to be borne by users once the renewable sources cannot supply adequate power to the grid in which they will pay an expensive fossil-based electric price. Moreover, fossil energy tends to gradually increase leading to economic conflict in society [2]. Therefore, in order to tackle this issue, an appropriate demand response scheme can be applied.Demand response is the change of electric usage by users due to change of electric price or maybe an incentive as a reward of lowering their power consumption [3]. Applying demand response to this hybrid system is very beneficial for shaving peak load demand [4,5], leading to the reduction of fossil energy consumption. Moreover, it can provide short-term impact and economic benefit for both consumer and utility.In order to support this demand response, short-term load forecasting (STLF) is very important for predicting whether the energy storage from renewable sources is able to handle the forthcoming power consumption or not. If the prediction states that the storage is not adequate to support the future load, then the electricity utility can announce this situation to the users, which eventually triggers them to reduce their electric usage, because users do not only want to pay more for conventional energy source but also want to get incentives from the authorities.Fortunately, with the help of developed infrastructures like smart meters equipped with a lot of sensors and the Internet of Things (IoT), a robust STLF method is feasible to...
