# Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
## 2 Literature Review
With the advent of machine learning-based predictions, research on energy consumption forecasting has rapidly advanced. ML algorithms such as Support Vector Machines (SVM), Decision Trees (DT), and Artificial Neural Networks (ANN) have been proven to be accurate for large datasets of individual buildings [8]. A specific investigation implemented a variety of machine learning (ML) algorithms, including Support Vector Machines (SVM) [9], Decision Trees (DT) [10], Random Forests (RF) [11], Boosting algorithms [12], K-Nearest Neighbors (KNN) [13], Gaussian Processes (GP) [14], and Multilayer Perceptrons (MLP) [15], to predict the energy utilization rate during the building design phase. Deep learning is an offshoot of machine learning that is founded upon the deployment of artificial neural networks, with a focus on developing and training multilayered network topologies aimed at discerning and isolating sophisticated patterns and attributes. The methodologies predominantly employed include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) frameworks, and Gated Recurrent Units (GRUs), among various other approaches. These techniques are progressively being leveraged to address an expansive spectrum of issues related to data analytics and pattern recognition, encompassing a diverse array of applications, from the analysis of images and videos to the processing of natural language and the recognition of auditory signals, facilitated by the diverse array of architectural and model-based solutions. Eyeriss v2 employs a hierarchical mesh on-chip network, designed to accommodate the extensive variation in layer shapes and sizes within DNNs. This architecture optimizes the utilization of computational resources through flexible adaptation to data reuse and bandwidth requirements. Specifically tailored for compact and sparse DNNs, Eyeriss v2\({}^{\prime}\)s accelerator design enables the direct processing of sparse data in the compressed domain, significantly enhancing both processing speed and energy efficiency, particularly when executing deep learning models on mobile devices [16]. [PERSON] and [PERSON] present a data-driven Artificial Intelligence (AI) model aimed at enhancing the energy efficiency of residential buildings in the UK [17]. Utilizing the Energy Performance Certificate (EPC) dataset for UK residential buildings and the Standard Assessment Procedure (SAP) guidelines, the study developed four distinct machine learning models to predict a building's energy performance across various retrofit scenarios. An interface was also designed, allowing users to analyze the impact of different retrofit strategies on a building's energy efficiency. The findings reveal that the Artificial Neural Network (ANN) model demonstrated the highest predictive accuracy with a coefficient of determination (R2) of 0.82 and a mean percentage error of 11.9%. [PERSON] conducted research on machine learning-based time series models for effective CO\({}_{2}\) emission forecasting in India [18]. She utilized three statistical models: the Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average with Exogenous factors (SARIMAX), and the Holt-Winters model. Additionally, she employed two machine learning models, namely Linear Regression and Random Forest, along with a Long Short-Term Memory (LSTM) model based on deep learning. The study concluded that the LSTM model is one of the most effective for predicting CO\({}_{2}\) emissions. [PERSON] and [PERSON] employed a metaheuristic search algorithm that simulated physical phenomena [19]. This algorithm guides the search process based on the forces of attraction and repulsion between objects. It is commonly used to address global optimization issues, seeking the optimal solution within the problem's solution space by simulating the motion of objects under mutual attraction and repulsion.