This paper presents the first publicly available smart meter dataset from Spanish households, consisting of 25,559 raw hourly time series spanning from November 2014 to June 2022, with an average length of nearly three years. The dataset includes three subsets derived from the raw data, each focusing on periods before, during, and after the COVID-19 lockdowns in Spain. It is a valuable resource for studying electricity consumption patterns and behaviors in response to various natural experiments, such as lockdowns, curfews, and changes in electricity pricing. Smart meters provide detailed information about energy consumption at the household level, enabling the modeling of energy systems, prediction of electricity consumption, and understanding of human behavior. The dataset is particularly useful for analyzing the impact of events like the pandemic on electricity usage patterns. It includes anonymized hourly electricity demand data from 25,559 supply points, primarily located in individual homes, but also in retail and department stores, offices, industrial plants, and public facilities. The dataset was collected by GoiEner, a Spanish electricity cooperative, and is part of the EU-funded WHY project, which aims to implement causal models to analyze energy consumption decisions and responses to interventions. The dataset is available on Zenodo and is licensed under the Creative Commons Attribution 4.0 International License. It includes raw data, processed data, and three subdatasets corresponding to periods before, during, and after the lockdowns. The dataset also includes metadata that provides relevant information for each entry. The dataset has been processed to ensure data quality, including data imputation, adjustment to local time, and segmentation into three periods related to the lockdowns. The data has been validated to confirm the presence of the three expected seasonal patterns in electricity consumption time series: daily, weekly, and annual. The dataset is a valuable resource for researchers studying electricity consumption patterns and behaviors in response to various natural experiments.This paper presents the first publicly available smart meter dataset from Spanish households, consisting of 25,559 raw hourly time series spanning from November 2014 to June 2022, with an average length of nearly three years. The dataset includes three subsets derived from the raw data, each focusing on periods before, during, and after the COVID-19 lockdowns in Spain. It is a valuable resource for studying electricity consumption patterns and behaviors in response to various natural experiments, such as lockdowns, curfews, and changes in electricity pricing. Smart meters provide detailed information about energy consumption at the household level, enabling the modeling of energy systems, prediction of electricity consumption, and understanding of human behavior. The dataset is particularly useful for analyzing the impact of events like the pandemic on electricity usage patterns. It includes anonymized hourly electricity demand data from 25,559 supply points, primarily located in individual homes, but also in retail and department stores, offices, industrial plants, and public facilities. The dataset was collected by GoiEner, a Spanish electricity cooperative, and is part of the EU-funded WHY project, which aims to implement causal models to analyze energy consumption decisions and responses to interventions. The dataset is available on Zenodo and is licensed under the Creative Commons Attribution 4.0 International License. It includes raw data, processed data, and three subdatasets corresponding to periods before, during, and after the lockdowns. The dataset also includes metadata that provides relevant information for each entry. The dataset has been processed to ensure data quality, including data imputation, adjustment to local time, and segmentation into three periods related to the lockdowns. The data has been validated to confirm the presence of the three expected seasonal patterns in electricity consumption time series: daily, weekly, and annual. The dataset is a valuable resource for researchers studying electricity consumption patterns and behaviors in response to various natural experiments.