Abstract
Short-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but without a systematic comparative analysis. In this paper, we first compare the most frequently used neural networks’ performance on the load dataset from the State Grid Sichuan Electric Power Company (China). Then, considering the current neural networks’ disadvantages, we propose a new architecture called a gate-recurrent neural network (RNN) based on an RNN for STLF. By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, particularly when the time scale is smaller than 20 min.
Highlights
Accurate short-term load forecasting (STLF) can play a significant role in power construction planning and power grid operation, and has crucial implications for the sustainable development of power enterprises
By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, when the time scale is smaller than 20 min
To explore the performance of different types of neural networks applied to STLF, we use four types: three types of the most commonly used neural networks and an improved neural network that we call the gate-recurrent neural network (RNN)
Summary
Accurate short-term load forecasting (STLF) can play a significant role in power construction planning and power grid operation, and has crucial implications for the sustainable development of power enterprises. STLF can predict future loads for minutes to weeks. Non-stationarity, and non-seasonality of STLF, it is very challenging to predict accurately. Inaccurate load forecasting may increase operating costs [1]. With an accurate electric load forecasting method, fundamental operating functions, such as unit maintenance, reliability analysis, and unit commitment, can be operated more efficiently [2]. It is essential for power suppliers to build an effective model that can predict power loads, accomplish a balance between production and demand, reduce production costs, and implement pricing schemes for various demand responses. According to the length of the forecast period, power load forecasting is divided into four categories: long-term load forecasting, medium-term load forecasting, STLF, and ultra-STLF [3]
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