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Higher Accuracy Leads to Improved Machine Learning for Energy Forecasting! Hive Power Reveals the Results of Its Weather Forecast Verification

Alexander Stauch - Head of Marketing
Dr. Alexander Stauch
Head of Marketing

The findings are stunning: the most accurate weather data and forecasts lead to superior energy generation and consumption forecasts, allowing companies to optimize their energy management, resulting in valuable cost savings, reduced planning risks and better decision making. In this story you will learn how Hive Power realizes these benefits with weather data provided by Meteomatics Weather API.

Setting the stage

Energy forecasting is of primary importance in day-to-day market operations. Short-term forecasting generally involves forecast horizons that range from a few minutes to a few days ahead. Energy Suppliers and Distribution System Operators benefit from accurate predictions of power demand and generation because they can optimally orchestrate their flexible assets to achieve their business goals.

Weather is king for energy companies

Weather is by far the most important external factor affecting energy consumption and generation. For this reason, Hive Power decided to conduct an internal research study to review and select the most performant numerical weather prediction provider.

One important criterion Hive Power considered was the availability of historical weather forecasts (that are available in Meteomatics API back until 1978). Many weather providers do not archive and store their predictions. However, historical weather forecasts are of crucial importance to train an energy prediction model. To be robust and reliable, a model should be trained on the same type of data that will be used at inference time.

Looking for the weather forecast benchmark

Hive Power looked at a dozen different providers, filtered out those that did not tick our boxes, and ended up with five finalists. Hive Power requested a year’s worth of hourly predictions of ground-level temperature and solar radiation generated for a single location at around midnight and covering from 24 to 48 hours ahead, which is the typical forecast horizon of Hive Powers’ energy prediction models. Hive Power compared these predictions with actual local observations and were stunned to discover Meteomatics’ API superior performance.

In the figures below, Hive Power shares its forecast verification results. In Figure 1, Hive Power plotted the distribution of the discrepancy between observed and predicted temperature. In Figure 2, Hive Power overlaid the five distributions for more convenient comparison. A similar situation was found for the solar radiation parameters. It became clear to Hive Power that Meteomatics’ API weather forecast data was the most accurate and well-calibrated.

Temperature: observed - forecasted

Distribution of the discrepancy between observed and predicted ground temperature for five different weather providers. The vertical dashed lines indicate the mean of each distribution (only Meteomatics' mean error is centred on zero). The Mean Absolute Error (MAE) is reported on each chart (the lower, the better).

Comparison: Meteomatics vs other providers

The same error distributions of Figure 1 overlaid (after estimating their kernel density). Meteomatics' curve is the narrowest and the only one that is zero-centred.

Gianluca Corbellini
Gianluca Corbellini
Managing Director
Hive Power
Meteomatics checked all the boxes and exceeded our expectations.

“We were looking for a one-stop-shop that could provide us with up-to-date and detailed weather data covering Europe, which needed to be conveniently available via a RESTful API. Hence why we were very excited about partnering with Meteomatics, and the commercial possibilities Meteomatics’ API could generate for Hive Power. We took quite some time to evaluate several numerical weather prediction providers. It was not an easy choice because of our demanding list of criteria, but Meteomatics checked all the boxes and exceeded our expectations. We were after a provider that covered the whole world, with a focus on Europe and especially the Alps, which are a challenging region when it comes to high-resolution weather forecasting. It was clear from our own forecast benchmarking exercise that Meteomatics was the most accurate weather forecaster, particularly over the short term. We were pleased to discover the rich plethora of standard and advanced weather parameters that Meteomatics’ API offers. Moreover, Meteomatics was one of the few providers that easily allowed the retrieval of archived historical weather forecasts, which are of critical importance to correctly train our machine learning models. We are thrilled to have switched to Meteomatics as our weather data provider of choice, and we foresee a long and fruitful partnership with them.’’, said Gianluca Corbellini, Managing Director at Hive Power.

The one stop-shop solution for all needs: Meteomatics Weather API

Meteomatics Weather API allows Hive Power to inform and enrich their proprietary energy consumption and production forecasting models. Powered by the latest and most advanced machine learning techniques, Hive Power’s Forecaster computes short-term probabilistic predictions to simplify how energy retailers and grid operators manage the aggregated energy production and consumption. Accurate energy predictions are crucial for effective peak-shaving, performant energy trading, robust grid stability, and avoidance of congestions.

About Hive Power

Founded in 2017 in Switzerland, Hive Power is a leading provider of innovative solutions for smart grids. Hive Power provides a SaaS platform to optimize your existing electrical distribution grid, both from the technical and economic points of view. Hive Power is implementing innovative techniques and business models to reach the customer-centric 4D energy future – Decarbonization, Digitalization, Decentralization, and Democratization.

Matthias Piot - Meteorologist and Client Manager
Dr. Matthias Piot
Meteorologist and Client Manager

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