Electric Load Forecasting Using Long Short-term Memory Algorithm

Abstract Power system load forecasting refers to the study or uses a mathematical method to process past and future loads systematically, taking into account important system operating characteristics, capacity expansion decisions, natural conditions, and social impacts, to meet specific accuracy requirements. Dependence of this, determine the load value at a specific moment in the future. Improving the level of load forecasting technology is conducive to the planned power management, which is conducive to rationally arranging the grid operation mode and unit maintenance plan, and is conducive to formulating reasonable power supply construction plans and facilitating power improvement, and improve the economic and social benefits of the system. At present, there are many methods for load forecasting. The newer algorithms mainly include the neural network method, time series method, regression analysis method, support vector machine method, and fuzzy prediction method. However, most of them do not apply to long-term time-series predictions, and as a result, the prediction accuracy for long-term power grids does not perform well. This thesis describes the design of an algorithm that is used to predict the load in a long time-series. Predict the load is significant and necessary for a dynamic electrical network. Improved the forecasting algorithm can save a ton of the cost of the load. In this paper, we propose a load forecasting model using long short-term memory(LSTM). The proposed implementation of LSTM match with the time-series dataset very well, which can improve the accuracy of convergence of the training process. We experiment with the difference time-step to expedites the convergence of the training process. It is found that all cases achieve significant different forecasting accuracy while forecasting the difference timesteps. Keywords—Load forecasting, long short-term memory, micro-gri

Edge and corner states in 2D non-Abelian topological insulators from an eigenvector frame rotation perspective

We propose the concept of 2D non-Abelian topological insulator which can explain the energy distributions of the edge states and corner states in systems with parity-time symmetry. From the viewpoint of non-Abelian band topology, we establish the constraints on the 2D Zak phase and polarization. We demonstrate that the corner states in some 2D systems can be explained as the boundary mode of the 1D edge states arising from the multi-band non-Abelian topology of the system. We also propose the use of off-diagonal Berry phase as complementary information to assist the prediction of edge states in non-Abelian topological insulators. Our work provides an alternative approach to study edge and corner modes and this idea can be extended to 3D systems