## 1 Introduction  
The issue of world climate change is a concern for various countries in the world. NOAA's 2020 Annual Climate Report states that the average rate of temperature increase of the world's surface has increased twice every ten years since 1981, compared to every ten years since 1880. This temperature increase can impact the increased demand for power consumption by about 2.7% for every 1 \({}^{\circ}\)C increase [1]. Therefore, power management possesses a crucial role in planning sustainable power supply policies. The first essential treatment is to forecast the power consumption of an area. The forecasting of power consumption can bring significant information to help estimate power demand for planning in the future. In addition, such forecasting of power consumption assists the powerhouse in formulating suitable and sustainable operations for electric power systems. A properly conducted forecasting can ensure that the supply will be able to meet the future demand for electrical power [2]. The work of power consumption forecasting can be categorized into two schemes, i.e., the traditional algorithm such as the autoregressive integrated moving average (ARIMA) model, and machine learning such as artificial neural networks (ANN). There are many studies performed that focus on power consumption forecasting at various scales. A comparison study of power consumption forecasting was performed during Korea's highest peak demands using various models involving traditional, machine learning, and hybrid models. The study result shows that machine learning models, such as long short-term memory (LSTM), and hybrid models, such as seasonal auto-regressive integrated moving average with exogenous factors (SARIMAX) and LSTM gain greater results over the traditional model [3]. In [4], the authors proposed a hybrid model for the month-ahead hourly power demand forecasting for medium industrial consumers. This model employed a non-linear autoregressive network with exogenous inputs (NARX) and artificial neural networks (ANN) to forecast daily power demand using timestamp datasets as exogenous variables and the output of NARX as LSTM-ANN input. Subsequently, a singular spectrum analysis and linear recurrent formula (SSA-LRF) model was proposed for Indonesian power forecasting [5]. This model was also combined with fuzzy or ANN frameworks to address nonlinearity of data. The result indicates that SSA-LRF-ANN is the accurate prediction model for such a related task. However, the existing studies in power forecasting encounter issues affecting forecasting accuracy. The main challenges are non-regularity distribution and various noise [6]. Several studies have utilized the STL method to decompose the time series data of power demands into seasonal, trend, and remainder components [6; 7; 8]. Afterward, these components are fed to the forecasting model as input. Several approaches have gained accurate results for power consumption forecasting with dynamic patterns. Nevertheless, they still need to be enhanced regarding the capture of abrupt changes, burstiness, and high levels of noise in future demands. Therefore, we propose a hybrid model of the RobustSTL and a temporal convolutional network (TCN) to forecast single time series data for hourly power consumption with reliability from interferences such as dynamic patterns, burstiness, and outliers. To obtain a comprehensive historical data understanding, the proposed model first decomposes the raw data using the RobustSTL model, instead of the standard STL. RobustSTL not only captures components such as STL results, but also identifies the residual spike and white noise in the remainder component [9]. These components then become input for the TCN. Through TCN, the proposed model can capture and learn the features of each component's information, although it has long dependencies [10; 11]. Based on the analysis above, this study focuses on building hourly power consumption forecasting using a combining model of RobustSTL and TCN.