This section discusses the space and energy.
We are living in a technology-driven world. Innovations make human life easier. As science advances, the usage of electrical and electronic gadgets are leaping. This leads to the shoot up of power consumption. Weather plays an important role in power usage. Even the outbreak of Covid-19 has impacted daily power utilization. Similarly, many factors influence the use of electricity-driven appliances at homes. Monitoring these factors and consolidating them will result in a humungous amount of data. But analyzing this data will help to keep track of power consumption. This system provides a prediction of usage of electric power at residences in the future and will enable people to plan ahead of time and not be surprised by the monthly electricity bill.
Natural Gas(NG) is one of the valuable ones among the other energy resources. It is used as a heating source for homes and businesses through city gas companies and utilized as a raw material for power plants to generate electricity. Through this, it can be seen that various purposes of NG demand arise in the different fields. In addition, it is essential to identify accurate demand for NG as there is growing volatility in energy demand depending on the direction of the government’s environmental policy. This project focuses on building the model of forecasting the NG demand and supply amount of South Korea, which relies on imports for much of its energy sources. Datasets for training include various fields such as weather and prices of other energy resources, which are open-source. Also, those are trained by using deep learning methods such as the multi-layer perceptron(MLP) with long short-term memory(LSTM), using Tensorflow. In addition, a combination of the dataset from various factors is created by using pandas for training scenario-wise, and the results are compared by changing the variables and analyzed by different viewpoints.