## 1 Introduction
In [20], an attention-based LSTM model with semantic consistency is used to transfer videos to natural sentences. An LSTM neural network is used in multimodal ambulatory sleep detection in [21], and the proposed method can synthesize temporal information accuracy. In [22], nonuniformly sampled variable length sequential data are classified, which is followed by regression by LSTM. The LSTM neural network also shows significant potential in the prediction field. The authors in [23; 24] proposed an LSTM-based framework for the single energy customer load forecasting. In [25], a multi-layer bidirectional RNN based on LSTM and a gated recurrent unit (GRU) is proposed for short-term load forecasting; the proposed method can match different types of load data and is shown to be more accurate. In [26], a forecasting model based on LSTM-DNN is proposed for the photovoltaic power output, available temperature data, and statistical features extracted from the historical photovoltaic output data using stationary wavelet transform. An LSTM neural network is proposed for the prediction of solar irradiance one hour in advance and one day in advance [27; 28]; the clearness index was used to classify the type of weather by k-means. A k-means LSTM network model for wind power spot prediction is proposed in [29]; the wind power factors are clustered to generate a new LSTM sub-prediction model. In [30], the authors proposed five LSTM-based forecasting methods for photovoltaic power prediction, and the prediction capacity is improved by stacking LSTM layers on top of each other. In [31] a one-dimensional convolutional stacked LSTM for load disaggregation is proposed; the deep learning framework is created by stacking several LSTM layers within the hidden layers. The hidden layers are joined reconnections in the LSTM cell. There is no gradient disappearance or gradient explosion problem in the prediction model of the stacked LSTM neural network. However, the long-distance data transmission will cause data loss, which will result in accumulated errors in the process prediction. In order to solve this problem, reverse computing combined with forward computing are introduced to solve the unidirectionality of the memory process in the process of training the data. A feedback mechanism is introduced to improve the front and back association. Combined with the reverse computing, the LSTM neural network has the ability of bidirectional computing, which can overcome the defection of data loss in long-distance transmission. Furthermore, forward and backward propagation prediction make data more dependent and reliable. A multi-layer stacked deep learning style is built for the data training process to improve the information communication between the dataset sequentially. The main contributions of this paper are as follows: (1) A bidirectional LSTM short-term load forecasting framework model is proposed in this paper, in which reverse computing is combined with forward computing to retrieve the important information hidden in the load profiles and improve the forecasting ability of the time-series problem. (2) A multi-layer stacked bidirectional LSTM prediction structure based on deep learning technology is proposed. The advantages of the multi-layer structure are applied to analyze the load profiles and extract the data essential features. (3) Last, the multi-layer stacked bidirectional LSTM prediction model is approved by using real operational cases, and the evaluation results are compared with other methods.