Machine learning for the power grid load forecast

Developing the smart grid, which applies a large amount of new technologies in power generation, transmission, distribution and utilization to achieve optimization of the power configuration and energy saving. As one of the key links to make a grid smarter, load forecast plays a significant role in planning and operation in power system.

In this project, we built the architecture based on the RNN, ANN and LSTM to tackle with various modeling requirements. In the modeling process, weather information, policy announcement and adjustment of weekdays/weekends are considered as the factors in the modeling. By model training and simulation, precise load results can be obtained. Then the operator can reasonably distribute the energy resources.