Household load forecasting provides great challenges as a result of high uncertainty in individual consumption of load profile. Traditional models based on machine learning tried to explore uncertainty depending on clustering, spectral analysis, and sparse coding with hand craft features. Recently, deep learning skills like recurrent neural network attempt to learn the uncertainty with one-hot encoding which is too simple and not efficient. In this paper, for the first time, we proposed a multitask deep convolutional neural network for household load forecasting. The baseline of one branch is built on multiscale dilated convolutions for load forecasting. The other branch based on deep convolutional autoencoder is responsible for household profile encoding. In addition, an efficient encoding strategy for household profile is designed that serves a novel feature fusion mechanism integrated into forecasting branch. Our proposed network serves an end-to-end manner in training and inference process. Sufficient ablation studies were conducted to demonstrate effectiveness of innovations and great generalization in point and probabilistic load forecasting at household level, which provides a promising prospect in demand response.
In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour meter, accurate price prediction becomes more and more crucial in the energy management and control of energy storage systems. Due to the great uncertainty of electricity price, the performance of the general electricity price forecasting models is not satisfactory to be adopted in practice. Therefore, in this paper, we propose a novel electricity price forecasting strategy applied in optimization for the scheduling of battery energy storage systems. At first, multiple nonstationary decompositions are presented to extract the most significant components in price series, which express remarkably discriminative features in price fluctuation for regression prediction. In addition, all extracted components are delivered to a devised deep convolution neural network with multiscale dilated kernels for multistep price forecasting. At last, more advanced price fluctuation detection serves the optimized operation of the battery energy storage system within Ontario grid-connected microgrids. Sufficient ablation studies showed that our proposed price forecasting strategy provides predominant performances compared with the state-of-the-art methods and implies a promising prospect in economic benefits of battery energy storage systems.
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