# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 4 Case Study
### Experimental Data and Evaluation Metrics  
This study deeply analyzes the effectiveness and performance of power load forecasting models by exploring several representative datasets. Each dataset provides a unique perspective and basis for validation, helping to comprehensively understand the complexities faced when forecasting electricity demand. These datasets vary in scope and geographic background, allowing the study to cover the characteristics of power load variations in different environments, further enhancing the adaptability and generalization ability of the forecasting model. Specifically, the study uses three main datasets: the Panama Case Study Dataset [43], the Daily Electricity Price and Demand Dataset (Victoria, Australia) [44], and the Household Electric Power Consumption Dataset [45]. All these datasets are publicly available and come from multiple authoritative sources, providing a solid data foundation for model validation and cross-regional comparisons. Through an in-depth analysis of these datasets, this study aims to provide more comprehensive and multidimensional research results in the field of power load forecasting. The dataset descriptions are as follows:  
The Panama Case Study Dataset: This dataset contains integrated information from multiple data sources with high data quality and rich features. The time span of the dataset is from 2015 to 2020, providing several years of historical power load data. The data granularity is recorded on an hourly basis, ensuring detail and precision. The dataset has been preprocessed, and all data sources have been merged into a CSV file indexed by date and time. Additionally, there are CSV files for load forecasting and two Excel files containing recommended regression variables and 14 training/testing dataset pairs. The data quality is high, though there may be some sparsity due to manual input for certain data, such as during holidays and school periods. Overall, this dataset offers comprehensive and detailed information for power load forecasting and is suitable for training and testing machine learning and forecasting algorithms. The forecasting time span is weekly, specifically starting from Saturday and ending on the next Friday. A 72 h unseen data gap must be considered during prediction, meaning that when predicting next week's load, only data recorded up to the last hour on Tuesday are used. The load data is recorded hourly, which helps capture short-term fluctuations and variations in power load, thus improving prediction accuracy. The Daily Electricity Price and Demand Dataset: This dataset covers 2016 days of data from 1 January 2015 to 6 October 2020, recording daily electricity demand and related variables in the state of Victoria, Australia. The data granularity is daily, and the dataset is of high quality, covering various aspects such as electricity demand, price, weather, and calendar information, providing comprehensive features for power demand forecasting. The dataset includes negative RRP (Regional Reference Price) data, reflecting the special market conditions during the pandemic, which adds to the dataset's diversity and research value. Overall, this dataset provides rich and detailed information for electricity demand forecasting and related research. The forecasting time span is from 1 January 2015 to 6 October 2020, totaling 2016 days. The load data are recorded daily, with each data point representing the total electricity demand of the day (in MWh). This daily granularity is suitable for medium- to long-term power demand forecasting and analysis, capturing seasonal changes and long-term trends in electricity demand. The Household Electric Power Consumption Dataset: This dataset records electricity consumption data for a household from December 2006 to November 2010, with a total of 2,075,259 measurement records. The data granularity is per minute, providing electricity consumption information for each minute. Approximately 1.25% of the measurements are missing, and missing values are represented by consecutive semicolons.