Hourly energy demand generation and weather

### Context
In a [paper released early 2019](https://arxiv.org/abs/1906.05433), forecasting in energy markets is identified as one of the highest leverage contribution areas of Machine/Deep Learning toward transitioning to a renewable based electrical infrastructure.

### Content
This dataset contains 4 years of electrical consumption, generation, pricing, and weather data for Spain. Consumption and generation data was retrieved from [ENTSOE a public portal](https://transparency.entsoe.eu/dashboard/show) for Transmission Service Operator (TSO) data. Settlement prices were obtained from the Spanish TSO [Red Electric España](https://www.esios.ree.es/en/market-and-prices). Weather data was purchased as part of a personal project from the [Open Weather API](https://openweathermap.org/api) for the 5 largest cities in Spain and made public here.

### Acknowledgements
This data is publicly available via ENTSOE and REE and may be found in the links above.

### Inspiration
The dataset is unique because it contains hourly data for electrical consumption and the respective forecasts by the TSO for consumption and pricing. This allows prospective forecasts to be benchmarked against the current state of the art forecasts being used in industry.

- Visualize the load and marginal supply curves.
- What weather measurements, and cities influence most the electrical demand, prices, generation capacity?
- Can we forecast 24 hours in advance better than the TSO?
- Can we predict electrical price by time of day better than TSO?
- Forecast intraday price or electrical demand hour-by-hour.
- What is the next generation source to be activated on the load curve?

{'Context': '在一篇于2019年初发布的论文中（https://arxiv.org/abs/1906.05433），将能源市场的预测识别为机器/深度学习在向基于可再生能源的电力基础设施转型过程中贡献最大的领域之一。', 'Content': '该数据集包含了西班牙4年的电力消耗、发电、定价和天气数据。消耗和发电数据是从[ENTSOE公共门户](https://transparency.entsoe.eu/dashboard/show)获取的输电服务运营商（TSO）数据。结算价格是从西班牙TSO[Red Electric España](https://www.esios.ree.es/en/market-and-prices)获得的。天气数据作为个人项目的一部分，从[Open Weather API](https://openweathermap.org/api)购买了西班牙5个最大城市的天气数据，并在此公开。', 'Acknowledgements': '这些数据通过ENTSOE和REE公开提供，可在上述链接中找到。', 'Inspiration': '该数据集的独特之处在于它包含了电力消耗的每小时数据和相应的TSO对消耗和定价的预测。这使得未来的预测可以与行业内正在使用的最先进预测进行基准测试。
- 可视化负载和边际供应曲线。
- 哪些天气测量和城市对电力需求和价格、发电能力影响最大？
- 我们能否比TSO更好地提前24小时进行预测？
- 我们能否比TSO更好地预测一天中不同时间段的电力价格？
- 预测日内价格或每小时电力需求。
- 在负载曲线上下一个将被激活的能源来源是什么？'}

搜集汇总

背景与挑战

背景概述

该数据集包含西班牙4年（约2015-2018年）的每小时电力消耗、发电、定价和天气数据，数据来自公开的能源运营商和天气API。其特点是多变量时间序列，涵盖能源与天气信息，适用于机器学习预测任务，如电力需求和价格预测，并提供了行业预测基准。