Zongxiang Chen

Smart microgrid is promising in providing a more affordable, efficient, and sustainable energy solution with increasing energy production from distributed renewable sources and diverse household electricity usage with large amounts of connected smart devices. Accurate prediction of the household electricity load and renewable energy production plays a significant role in achieving optimized efficiency of the microgrid. Meanwhile, the privacy and security of data sharing over the smart grid are crucial. This paper proposes a blockchain-enabled microgrid Internet of Things (MIoT) with accurate predictions of renewable energy production and household electricity load. The blockchain framework can guarantee the privacy and security of data sharing over the microgrid. An improved model by stacking long short-term memory (LSTM) and gated recurrent units (GRUs) is proposed for energy generation and electricity load predictions using historical data in the microgrid and the weather forecasting data. The sparrow search algorithm optimized by Levy flights (LevySSA) is used to optimize the hyperparameters of the stacked LSTM-GRU method. The experimental results verify the accuracy and robustness of the proposed method in the prediction of electricity load and renewable energy production for effective smart microgrid operation. For PV forecasting, the proposed LevySSA-LSTM-GRU achieves nRMSE = 0.0535, nMAE = 0.0455, and R² = 0.9898, outperforming the strongest baseline. For load forecasting, averaged over four test intervals, it yields nRMSE = 0.1034, nMAE = 0.0836, with R² = 0.9340, demonstrating consistent superiority compared with conventional baseline models. Overall, the proposed framework enables secure data sharing and high-accuracy forecasting, offering strong potential to support real-time energy management and operational optimization in smart microgrids.

Photovoltaic (PV) power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder (SAE) and Bayesian regularized NARX network (BRNARX) for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance (KMDDPC) is applied to classify the dataset into multiple clusters, including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation (MKDE) to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE‐BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R² for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC‐MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods.