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
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.
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.
Figure 1 - 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).
Figure 2 - 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.
More details can be found in Hive Power’s blog article. Alternatively, you can hear more by attending our webinar taking place on 26th October, register to attend by clicking this link