From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, non-smooth, non-linear and noisy. To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model contains a similar day selection approach involving the LightGBM and k-means algorithms. Compared to the traditional RNN-based model, our proposed model can avoid falling into the local minimum and outperforming the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia. The performance of our model has an average MAPE (mean absolute percentage error) of 1.13, where RNN is 4.18, and LSTM is 1.93.
Microscopic
imaging of molecules is important for determination
of molecular structures by providing real space snapshots. The scanning
tunneling microscope (STM) is able to offer local perturbations to
a molecule such that the molecule is locked in a transient state.
This perturbation is tunable to certain extent, giving rise to the
possibility to “pose” the molecule before a shot is
taken. Here, we report the pose-and-shoot method applied to Au-adatom-diethylthiolate
(CH3CH2S–Au–SCH2CH3). This type of perturbational imaging significantly enhances
the capability of the STM in providing a dynamic view of the molecule.
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