Tutorial on time series prediction using 1D-CNN and BiLSTM: A case example of peak electricity demand and system marginal price prediction

Although research on time series prediction based on deep learning is being actively carried out in various industries, deep learning technology still has a high entry barrier for researchers who have not majored in computer science. This paper presents a tutorial on time series prediction using a deep learning-based model. The entire process of time series data prediction is presented—from data collection to evaluation of prediction results. The details of each step are shown through a case example of predicting peak electricity demand and system marginal price of Jeju Island in Korea using the 1D-CNN and BiLSTM model. In Jeju Island, the proportion of renewable energy in the total power generation capacity is increased to 67% in 2021, requiring more accurate electricity demand forecasts. Therefore, using 808 days of training data from February 2018, electricity demand and SMP for the next 21 days were predicted. To make it easier for readers to follow, the example uses only open public data, and the entire Python source code is shared via a GitHub repository. The prediction error calculated by WRMSSE showed 0.42 in electricity demand and 0.63 in SMP max. A WRMSSE value less than one means that the forecast was relatively good, that is, better than naïve forecasting. This tutorial is not limited to the energy industry but can be utilized for any application requiring time-series data prediction. This article is expected to be of great help to researchers who need to understand the process of time series prediction using deep learning and use it for application in their industry.