# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 4 Experiments
### Setup  
#### 4.1.1 Datasets  
We perform experiments on two public and one private dataset for empirical studies. All datasets are available for online access. Because this research focuses on univariate time-series forecasting, we only study time series with a single dimension for each dataset above. Table 3 presents an overview of the corpus statistics. Individual household electric power consumption, Dataset 1 is available online ([[https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption)]([https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption)), accessed on 17 July 2022). It contains minute-by-minute electric power consumption in one household in France for 47 months (December 2006 to November 2010). The time series includes the total active power consumed, total reactive power consumed, average current intensity, active energy for the kitchen, active energy for the laundry, and active energy for climate control systems [19]. In total, we have 2,075,259 multivariable sequences. For this dataset, 34,589 univariable sequences were used as the study value for the hour-global active power data. The energy consumption curves of 499 customers from Spain, Dataset 2 is available online ([[https://fordatis.fraunhofer.de/handle/fordatis/215](https://fordatis.fraunhofer.de/handle/fordatis/215)]([https://fordatis.fraunhofer.de/handle/fordatis/215](https://fordatis.fraunhofer.de/handle/fordatis/215)), accessed on 17 July 2022). The dataset contains hourly energy consumption data, outside temperatures for the region, and the metadata for 499 customers in Spain for about one year (1 January 1 to 31 December 2019). The entire dataset consists of 8760 data points. The energy consumption data was used for this dataset as the study value. The CNU energy consumption, Dataset 3 is available online ([[https://github.com/andrewlee1807/tcns-with-nas/tree/main/Dataset/cnu-dataset](https://github.com/andrewlee1807/tcns-with-nas/tree/main/Dataset/cnu-dataset)]([https://github.com/andrewlee1807/tcns-with-nas/tree/main/Dataset/cnu-dataset](https://github.com/andrewlee1807/tcns-with-nas/tree/main/Dataset/cnu-dataset)), accessed on 5 August 2022). It contains a real-world dataset with energy consumption values of 90 locations at CNU, collected continuously hourly for 1.3 years (from 1 January 2021 to 14 January 2022). Each location has information for 11,232 data points. In this research, we focus on the total electricity consumption of a particular location: Engineering-Building-07.monitor_02. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Datasets** & **Length of Time Series** & **Total Number of Variables** & **Attributions** \\ \hline \multirow{6}{*}{Dataset 1} & & & Global active power \\  & & & Global reactive power \\  & & & Voltage \\  & & & Global intensity \\  & & & Submetering 1 \\  & & & Submetering 2 \\  & & & Submetering 3 \\ \hline Dataset 2 & 8760 & 2 & Energy consumption \\  & & & Outside temperature \\ \hline Dataset 3 & 11,232 & 1 & Energy consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset statistics. For each dataset, it is necessary to conduct preprocessing procedures before the training process because the power values in the datasets are relatively high; for example, in Dataset 3, _mean_ = 130.48 and _std_ = 46.97. Therefore, to avoid overflow, increased computational cost, and dataset distortion, each dataset refers to the rescaling of the features to a range of [0, 1] using the min-max normalization, calculated as follows (5):  
\[z_{i}=\frac{x_{i}-\min(x)}{\max(x)-\min(x)} \tag{5}\]  
where \(x=(x_{1},\ldots,x_{n})\) and \(z_{i}\) is the \(i^{th}\) normalized data point. We also evaluated the time series forecasting task on three datasets in this experiment.