A Multiple Time Series-based Recurrent Neural Network For Short-term Load Forecasting

Electricity,is an indispensable resource in daily life and industrial production.It is related to the national economic construction and the steady development of various industries.However,the production and consumption of electricity resources should be synchronization due to non-mass storage.It is important that the accurate forecasting of load for both the utility and the energy sector,especially for short-term load forecasting(STLF).In the past research,taking main the climatic conditions,historical power load changes,and other factors into account,while the dependence analysis between the various factors become very difficult when we deal with the more complex and diversified data structures and rapid changes,result to the forecast accuracy and stability not be guaranteed.In this thesis,we propose a recurrent neural network model based on the deep learning framework to analyze the short-term load forecasting.Firstly,we propose the concept of multiple time series,according to the different time-steps,we divide the historical power load into continuous sequences like short-term series and long short-term series,as well as discrete sequences like cycle series and cross long short-term series with jumping time-steps.Then,a deep recurrent neural network model is used to train the single and multiple time series and combine learning;Finally,we test and verify it by testing stage.Experiments show that the combined model has high forecasting performance presented in this thesis and superior to other methods in the same data set.The best value mean absolute percent error(MAPE)is 0.71,the other methods best value are 1.03.At the same time,we also found that the relationship of dependency between different time series and which impact for short-term load forecasting.Namely,the continues sequences on the short-term load forecasting have good results while the discrete sequences consequence is bad.However,it can also strengthen the final performance on short-term load forecasting when we combine them by deep learning train process.