# Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
## 2 Literature Review  
Predicting the electrical energy utilization of buildings is crucial for enhancing energy efficiency and improving decision-making to reduce electricity demand. (1) Predicting energy consumption means being able to understand in advance future energy demand and arrange energy use reasonably according to the prediction results. For example, the energy consumption of a building is affected by many factors, such as weather changes, human activities in the building, and the use of equipment. By accurately modeling and predicting these factors, managers can take measures in advance to ensure a balance between energy supply and demand and avoid unnecessary energy losses [3]. (2) Energy consumption prediction can help optimize the timing and quantity of the energy supply. For example, by predicting the peaks and troughs of electricity consumption in a building, managers can adjust the energy system in advance during peak hours to avoid power shortages or reduce the energy supply during troughs to avoid waste. This is particularly important for dynamic energy-consuming systems such as heating, ventilation, and air conditioning systems (HVAC). (3) Energy consumption forecasting can help optimize equipment operation. For example, air conditioning sys  
Figure 1: CO\({}_{2}\) emissions by fuel from 1950 to 2022. Data source: [PERSON], [PERSON], & [PERSON] (2020). CO\({}_{2}\) emissions by fuel. Our World in Data. Retrieved from [[https://ourworldindata.org/](https://ourworldindata.org/)]([https://ourworldindata.org/](https://ourworldindata.org/)) emissions-by-fuel (accessed on 9 February 2022). tems, lighting systems, and other equipment often consume a lot of energy. By accurately predicting the demand for these devices at different times of the day, long periods of idle operation or overuse can be avoided, and the start and stop times of the equipment can be optimized, thereby reducing energy waste [4]. (4) Energy consumption forecasts can help prevent energy systems from being overloaded or operating inefficiently. For example, during periods of peak demand, the electricity system may be overloaded, which increases energy consumption. By forecasting these peak demands, measures can be taken in advance to regulate the load, reducing system stress and energy waste. At the same time, forecasting energy consumption can also help identify equipment or systems that are operating inefficiently, so that maintenance or optimization can be carried out in a timely manner to avoid energy waste [5]). (5) Energy consumption forecasts can also provide a scientific basis for governments or businesses to formulate effective energy management policies. By analyzing the energy consumption data of different buildings, governments can propose energy-saving regulations or incentive policies in a targeted manner to promote energy efficiency. In addition, energy suppliers can adjust their pricing strategies based on the forecasts to encourage the use of energy during off-peak hours and reduce waste during peak hours [6]. Due to all of these benefits, research into building energy consumption has increased sharply in recent years. The construction of electrical energy prediction models for buildings is typically divided into two approaches: forward strategies and data-driven strategies. Forward models, commonly known as physics-based modeling methods, often require a comprehensive set of explanatory inputs related to the building's architecture and its environmental context. The surrounding environment includes information on HVAC systems, weather data, thermal characteristics, etc. [7], which can be quite complex. Data-driven models, particularly black-box models, have the primary advantage of being more efficient and flexible in the modeling process. The use of advanced data analysis techniques, such as machine learning and artificial intelligence, enables data-driven models to achieve high accuracy and discover relationships between variables that may be useful but were previously unknown through efficient computation.