Author ORCID Identifier

Defense Date


Document Type


Degree Name

Master of Science


Electrical & Computer Engineering

First Advisor

Zhifang Wang



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-grid


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