# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 1 Introduction  
Research on energy usage management has a long tradition, especially about energy consumption. According to Enerdata ([[https://yearbook.enerdata.net/electricity/electricity-domestic-consumption-data.html](https://yearbook.enerdata.net/electricity/electricity-domestic-consumption-data.html)]([https://yearbook.enerdata.net/electricity/electricity-domestic-consumption-data.html](https://yearbook.enerdata.net/electricity/electricity-domestic-consumption-data.html)), accessed on 18 July 2022) record in 2021, electricity accounts for 10% of all types of global energy, mostly used by China (42%), the United States (21%), and India (7%). Forecasting models are necessary to optimize an energy consumption program to reduce energy loss efficiently. Many forecasting models, both classical statistical methods and deep learning, are available.  
The time-series forecasting problem is usually overcome by classical statistical methods, such as autoregressive (AR) [1] models and the Gaussian process [2], or methods using deep learning, such as LSTM, GRU, and Transformers [3]. Although the above methods operate well in specific circumstances [4], their potential in practice is limited due to heavy computation.  
In this paper, we propose the stride-dilated temporal convolutional networks (TCNs), a family of lightweight models for predicting energy consumption. The proposed method is based on the periodic patterns of time series that are usually visible in energy consumption data. Given the capability of detecting periodic patterns, the proposed model is capable of automatically focusing on learning the important parts of data to make predictions. To predict a period in the future, we focus on extracting information from moments in the past. We hypothesize that there are only a few important time points when forecasting, and most of the history has a low correlation; therefore, we can ignore it to reduce the computational burden. Moreover, we observe that different time series data have cyclic patterns that differfrom others. Therefore, we suggest using a search algorithm to determine an optimized TCN architecture with appropriate stride hyperparameters; thus, this paper adopted Bayes optimization.  
The aim of this work is two-fold:  
* We propose a new TCN architecture with performance on par with state-of-the-art models on several benchmarks, but the number of parameters is greatly reduced based on the stride mechanism. We search for the best stride hyperparameter, representing cyclic patterns on the data.
* We introduce a dataset of electrical energy consumption measured at Chonnam National University (CNU), South Korea. Along with the dataset, we also present the baseline benchmark.  
The rest of the paper is organized as follows: Section 2 presents the background work. Section 3 details the methodology, and Section 4 describes the experiments on the dataset. Then, Section 5 discusses the work and presents a conclusion. Additionally, we publish the source code and datasets used for the experiments ([[https://github.com/andrewlee1807/tcns-with-nas](https://github.com/andrewlee1807/tcns-with-nas)]([https://github.com/andrewlee1807/tcns-with-nas](https://github.com/andrewlee1807/tcns-with-nas))).