Integration of Ensemble GoogLeNet and Modified Deep Residual Networks for Short-Term Load Forecasting

Due to the strong volatility of the electrical load and the defect of a time-consuming problem, in addition to overfitting existing in published forecasting methods, short-term electrical demand is difficult to forecast accurately and robustly. Given the excellent capability of weight sharing and feature extraction for convolution, a novel hybrid method based on ensemble GoogLeNet and modified deep residual networks for short-term load forecasting (STLF) is proposed to address these issues. Specifically, an ensemble GoogLeNet with dense block structure is used to strengthen feature extraction ability and generalization capability. Meanwhile, a group normalization technique is used to normalize outputs of the previous layer. Furthermore, a modified deep residual network is introduced to alleviate a vanishing gradient problem in order to improve the forecasting results. The proposed model is also adopted to conduct probabilistic load forecasting with Monte Carlo Dropout. Two acknowledged public datasets are used to evaluate the performance of the proposed methodology. Multiple experiments and comparisons with existing state-of-the-art models show that this method achieves accurate prediction results, strong generalization capability, and satisfactory coverages for different prediction intervals, along with reducing operation times.

Risk Analysis of Voltage Violation With PHEV Inter-Area Mobile Charging Strategy Under Gas Station Networks Attacked

To reduce the risk of voltage violation after gas station networks (GSNs) are attacked, this study investigates an inter-area mobile charging strategy of plug-in hybrid electric vehicles (PHEVs) to decrease the charging load by taking full advantage of charging resources. First, considering the location of the charging station, the waiting time, and the charging fee, an inter-area mobile charging strategy of PHEVs is proposed, and a mobile charging model of PHEVs among regions is established to relieve the charging pressure. Second, the risk index is developed to analyze the risk of voltage violation in terms of the results of probabilistic load flow (PLF). Finally, the proposed strategy is tested on a modified coastal active power distribution network, and simulation results show that the charging load of PHEVs is dispersed among regions and the risk of voltage over-limit can be reduced.

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.