To address the strong non-stationarity, coupled multi-scale fluctuations, and insufficient parameter adaptability of net load forecasting models under high photovoltaic penetration, an ICEEMDAN-BO-BiGRU combined short-term net load forecasting model is proposed. First, the net load sequence is constructed from the actual load and photovoltaic output, and then decomposed into eight intrinsic mode function components and a residual component using improved complete ensemble empirical mode decomposition with adaptive noise. Second, a bidirectional gated recurrent unit forecasting submodel is developed for each component, and Bayesian optimization is employed to adaptively optimize the key hyperparameters. Finally, the forecasts of all components are linearly summed to obtain the final net load forecast. Experiments are conducted using the CN03 sample from the HEEW dataset. The results show that, compared with CEEMDAN-BiGRU, the proposed method reduces the MAE and RMSE by 37.84% and 35.73%, respectively, over the complete February 2022 test period, while achieving a mean R2 of 0.9783, indicating lower observed forecasting errors under the reported experimental configurations.
Short-term photovoltaic (PV) power forecasting faces challenges from strong randomness and non-stationarity. This paper proposes a combined ICEEMDAN–BiLSTM–Attention–IMSO forecasting framework. First, ICEEMDAN decomposes the historical PV sequence into components, which are reorganized into three entropy groups based o...
Xin-Yi Li, Fang-Lin Wang, Hang Liu· Energies· 0 citations
Short-term electric load forecasting is crucial for the daily scheduling and market trading of power systems, ensuring their stable operation. However, the randomness and uncertainty of load series make accurate forecasting exceptionally challenging. To enhance the forecasting accuracy, a novel MPRF residual fusion mod...
To address the challenges of low accuracy and poor generalization in energy time series forecasting, which stem from its nonlinear, non-stationary, and limited-sample-size nature, this study proposes a hybrid forecasting framework. The model was designed to be highly accurate and robust, thereby enhancing the relia...
Mao-Die Luo, Zuo-Min Zhu, Jin-Hai Guo et al.· Grey Systems Theory and Appl...· 0 citations
The proposed short-term coupled forecasting model of power loads based on AI technology demonstrates high accuracy and robustness across different time scales and is helpful for the power industry to better cope with power market fluctuations and changes.
Li-Ling Peng, Tong Li, Guo-Feng Fan et al.· Computer Modeling in Enginee...· 0 citations
Accurate short-term net load forecasting is challenging due to the superimposition of meteorological influences, daily and weekly seasonal patterns, and irregular disturbances in the observed series. On the one hand, redundant meteorological input data risks hindering the learning process. On the other hand, methods th...
User-level short-term load forecasting is crucial for the efficient operation and dispatch of distribution networks. However, accurate forecasting remains challenging because of pronounced load fluctuations and abrupt local variations. To address this challenge, we propose a short-term load forecasting method based on...
