Welcome to alitiq's Blog about all topics about Energy Forecasting. From AI architecture to Servers and eXchange.
** Ready to start forecasting load efficiently?** Setting up accurate load locations is the first step toward unlocking smarter demand insights—whether it's electricity, gas, or district heating.
In the quest for accurate wind power forecasting, there's one reality that often gets in the way: real-world power output does not always reflect the full capacity of your wind turbines or pv systems. This discrepancy is frequently caused by curtailments and unavailabilities.
Parsing weather forecasts from our Weather API is easy and just needs some lines of code. You need to know which weather forecasting model and the location you want to get forecasts for.
In our general Documentation about the API here, we described the way to setup / configure your PV-System in a high-level way. To give you a more detailed view into the way we think about PV-Systems and how you can boost the performance by just following this guide, keep reading.
Renewable energy forecasting plays a crucial role in the sustainable energy ecosystem. However, the inherently variable nature of renewable sources like solar and wind presents a unique challenge: uncertainty. Tackling this uncertainty effectively can help improve energy grid stability, reduce costs, and foster efficient resource allocation. In this article, we'll dive into uncertainty quantification (UQ) in renewable energy forecasting, exploring its sources, methods, and benefits. Let’s navigate this exciting landscape! 🚀
Forecasting is a cornerstone of the energy sector, enabling decision-making for operations, investments, and policy development. However, the real value of a forecast lies in its evaluation and cross-validation. How well did it predict reality? Here’s a step-by-step guide to evaluating forecasts, focusing on understanding the process, selecting the right forecasts, and comparing them against observations.
Despite being a very popular metric for measuring forecast accuracy in forecasting, MAPE certainly has its strengths and limitations that anyone using it should take into consideration. This deep review of the efficacy of MAPE for measuring forecast accuracy in any kind of forecasting task like heat demand, solar- or wind power and inspect the metric’s behavior in different scenarios. For scenarios where MAPE is not suitable, alternative metrics are discussed.
Let's assume you are having your observation/measurement data in in a pd.DataFrame or in any kind of a file-like object ready to push to an external sFTP. Here we show you how to push the data to the alitiq sFTP.
Welcome to the alitiq-solar Application! Nice that you have found your way to us. Here we give you a tiny and quick introduction into our solar web application and guide you through the first steps you need to setup your portfolio.