# Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method
Deep Learning [12], a subfield of machine learning, utilizes multilayer neural networks to simulate the way the human brain processes information, automatically extracting features from data for prediction or classification tasks. Compared to traditional machine learning methods [13], deep learning is capable of handling more complex tasks such as image recognition, speech recognition, and natural language processing. The core concept involves using multiple layers of neural networks, where each layer progressively extracts abstract features from the data, thereby achieving higher accuracy on large datasets. Key technologies in deep learning include Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Autoencoders, and Transformers. These techniques have made significant advancements in various fields, driving the rapid development of artificial intelligence. Artificial Neural Networks (ANNs) [14] form the foundation of deep learning, mimicking the functioning of biological neurons. An ANN consists of an input layer, one or more hidden layers, and an output layer, with full connections between layers. This structure allows ANNs to achieve complex nonlinear mappings. In the context of energy consumption prediction, ANNs can capture intricate relationships between operational parameters and energy usage. However, due to their high model complexity, ANNs, despite often performing well on training data, may exhibit poor generalization to unseen data. Furthermore, in the absence of substantial feature engineering, ANNs may struggle to extract useful information from raw data. Convolutional Neural Networks (CNNs) [15], initially designed for image processing, can also be applied to energy consumption prediction for time series data. By utilizing convolutional layers to extract local features, CNNs can capture spatial or temporal dependencies within the data. In energy consumption prediction, CNNs are effective in identifying local patterns in drilling pump operation data, such as energy consumption variations over specific time periods. Nevertheless, while CNNs excel at capturing short-term dependencies, their performance deteriorates when dealing with long-term dependencies, limiting their applicability to certain complex time series prediction tasks. Recurrent Neural Networks (RNNs) [16] are particularly well suited for handling sequential data. Unlike traditional feedforward neural networks, RNNs share parameters across the temporal dimension through their recurrent structure, enabling them to capture dependencies in time series data. This characteristic makes RNNs highly effective in tasks such as time series data analysis, speech recognition, and natural language processing. However, when dealing with long sequences, RNNs are prone to issues such as vanishing or exploding gradients, which can make the model difficult to train and impair its ability to effectively capture long-term dependencies. The primary advantage of Long Short-Term Memory (LSTM) [17] networks lies in their ability to effectively handle long-term dependencies in time series data. The energy consumption of drilling pumps is a typical time series problem, where the current energy state is influenced not only by the present operational parameters but also by past operating conditions and process parameters over a period of time. Although traditional Recurrent Neural Networks (RNNs) are capable of processing time series data, they often suffer from vanishing or exploding gradient problems when dealing with long sequences, resulting in poor performance in capturing long-term dependencies. As a deep learning model, LSTM can automatically extract features from data without relying on manual feature engineering. Traditional empirical models and statistical methods typically depend on handcrafted features and are based on linear assumptions.