印度是世界上可再生能源增长最快的市场之一。到2022年，政府已为今年的175吉瓦斯设定了雄心勃勃的可再生能源目标，比2014年增长了400％，来自太阳能光伏电动机100 GW，另外60 gw的Wind。但是，即使印度希望添加这些新的风和太阳能电厂，它仍在努力吸收它已经产生的可再生能源。

India’s leading wind power state, Tamil Nadu, is a例子。尽管可用的风能大量可用，但预期在接下来的24小时内将产生多少风能的能力有限，这意味着传统的发电厂保持运行和堵塞的传输线路。如果网格操作员有更好的预测，这些植物可能会被升级甚至关闭，并将更多的风能发送给客户。

为什么预测很重要？答案始于网格操作员的基本工作，即balance electricity supply and demand, every minute of the day. To do this, grid operators schedule power generation one day in advance, committing the power plants under their authority to an hour-by-hour timetable of when to run and supply electricity. Getting that balance wrong results in停电或电力激增。

This daily balancing commitment is partly for technical reasons -- some types of power plants, notably coal, take more time to warm up – but it’s also partly economic, because fossil generation companies don’t want to generate power and burn expensive fuel without a guarantee of being paid. The other important factor is the availability and capacity of the transmission network, to transmit power from generation plants and send it to consumers. Network traffic jams can make this difficult.

Not Just Variable – Unpredictably Variable

All this works great, if both supply and demand are perfectly predictable. Decades ago, the unpredictability was mostly on the demand side: grid operators knew how much coal, large hydro, nuclear and gas power they could turn on, but they didn’t know for sure how much power their customers would use the next day.

Over time, increasingly accurate computer models based on historical data and other factors were able to predict how much power consumers would use. Today’s load forecasting models in several parts of the world have an impressively low error rate of小于5％。

但是，就像需求侧的预测受到控制一样，供应方面的复杂性是以可变的可再生能源技术（如风能和太阳能）的形式出现的。除了它们的环境福利外，重大的技术进步和降低成本也使可再生能源成为多个国家的主要能源选择。

问题是这些技术不仅是可变的，它们是不可预测多变的。相对很难知道这些发电厂将在第二天产生多少能量。结果，电网运营商需要将额外的发电厂（通常是基于化石）识别为备份。少量，这与化石燃料厂所需的额外备用资源相同。但是，关于风和太阳能的不确定性越大，需要备份植物越多，成本就越大。

This is where the real value of renewable energy forecasting comes in. Researchers have been努力工作多年在计算机型号上，可以更准确地预测第二天（小时）将产生多少风能和太阳能。这些模型使用天气数据，历史模式，有关风电厂技术特征的信息以及其他数据来计算生成功率的预测。

As these models get more accurate, grid operators canreduce the amount of back-up generation they need to keep online, and thus reduce costs. A 10 percent improvement in accuracy of wind forecasting on the western U.S. grid would每年节省数千万美元, increasing to $100 million dollars per year for higher levels of wind penetration. Colorado-based Xcel Energy, an early adopter of wind forecasting models, saw$40 million in savings over four years。

印度的陷阱和对可再生能源预测的承诺

Unfortunately, India’s adoption of advanced wind and solar forecasting has been slow. In 2013, the Central Electricity Regulatory Commission (CERC) tried to address this problem by requiring all wind and solar plant operators to make day-ahead predictions for the amount of power they would generate. Wind plant predictions that were off by more than 30 percent triggered a fine.

Wind power developers反对，认为这项技术还没有准备好，因此不可能遵守和该规则在2014年被暂停。This year, CERC introduced a more robust草稿框架加强在可再生能源领域的预测。

During this period, India’s National Institute of Wind Energy has been working to introduce more accurate wind forecasting. Recently, it has与基于巴塞罗那的风建模公司Vortex合作为了支持泰米尔纳德邦的国有公用事业公司Tangedco提供准确的风预测。这项工作似乎是取得进展，尽管该系统尚未完全实施。

因此，现在的问题是，这项有希望的飞行员在风预测上是否可以扩展以覆盖印度大部分或全部风力发电的生产，而且先进的预测模型是否可以在印度达到与其他国家 /地区相同的准确性。如果是这样，它将有助于使该国更加坚定地实现其雄心勃勃的可再生能源目标。