Wenhao Chen

Accurate short-term load forecasting (STLF) plays an essential role in sustainable energy development. Specifically, energy companies can efficiently plan and manage their generation capacity, lessening resource wastage and promoting the overall efficiency of power resource utilization. However, existing models cannot accurately capture the nonlinear features of electricity data, leading to a decline in the forecasting performance. To relieve this issue, this paper designs an innovative load forecasting method, named Prophet–CEEMDAN–ARBiLSTM, which consists of Prophet, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the residual Bidirectional Long Short-Term Memory (BiLSTM) network. Specifically, this paper firstly employs the Prophet method to learn cyclic and trend features from input data, aiming to discern the influence of these features on the short-term electricity load. Then, the paper adopts CEEMDAN to decompose the residual series and yield components with distinct modalities. In the end, this paper designs the advanced residual BiLSTM (ARBiLSTM) block as the input of the above extracted features to obtain the forecasting results. By conducting multiple experiments on the New England public dataset, it demonstrates that the Prophet–CEEMDAN–ARBiLSTM method can achieve better performance compared with the existing Prophet-based ones.

Short-term load forecasting is a key digital technology to support urban sustainable development. It can further contribute to the efficient management of the power system. Due to strong volatility of the electricity load in the different stages, the existing models cannot efficiently extract the vital features capturing the change trend of the load series. The above problem limits the forecasting performance and creates the challenge for the sustainability of urban development. As a result, this paper designs the novel ResNet-based model to forecast the loads of the next 24 h. Specifically, the proposed method is composed of a feature extraction module, a base network, a residual network, and an ensemble structure. We first extract the multi-scale features from raw data to feed them into the single snapshot model, which is modeled with a base network and a residual network. The networks are concatenated to obtain preliminary and snapshot labels for each input, successively. Also, the residual blocks avoid the probable gradient disappearance and over-fitting with the network deepening. We introduce ensemble thinking for selectively concatenating the snapshots to improve model generalization. Our experiment demonstrates that the proposed model outperforms exiting ones, and the maximum performance improvement is up to 4.9% in MAPE.

Short-term load forecasting (STLF) is essential for urban sustainable development. It can further contribute to the stable operation of the smart grid. With the development of renewable energy, improving STLF accuracy has become a vital task. Nevertheless, most models based on the convolutional neural network (CNN) cannot effectively extract the crucial features from input data. The reason is that the fundamental requirement of adopting the convolutional neural network (CNN) is space invariance, which cannot be satisfied by the received data, limiting the forecasting performance. Thus, this paper proposes an innovative ensemble model that comprises a densely residual block (DRB), bidirectional long short-term memory (Bi-LSTM) layers based on the attention mechanism, and ensemble thinking. Specifically, the DRB is adopted to extract the potential high-dimensional features from different types of data, such as multi-scale load data, temperature data, and calendar data. The extracted features are the input of the Bi-LSTM layer. Then, the adopted attention mechanism can assign various weights to the hidden state of Bi-LSTM and focus on the crucial factors. Finally, the proposed two-stage ensemble thinking can further improve model generalization. The experimental results show that the proposed model can produce better forecasting performance compared to the existing ones, by almost 3.37–5.94%.