# Technology Readiness Levels (TRLs) in the Era of Co-Creation
## 3 The Case of an AI-Based Energy Forecasting Technology  
TRUST-AI (www.trustai.eu, accessed on 14 April 2024) is a Horizon-funded research project running in the period 2020-2025 and aiming to develop a framework of so-called explainable AI approaches and their validation in diverse use cases. One such case was in the energy domain [40,41], where our specific responsibility was to customize an explainable energy forecasting approach. Though a variety of AI forecasting models have been developed over the last twenty years, our forecasting approach would differ in that it could provide user explanations of the forecast. Explainability refers both to model level explainability achieved via symbolic expressions and instance explainability, which, in our case, was centered on the concepts of feature importance and counterfactuals. This explainability would create confidence on the side of users, and this confidence is something often considered essential in the literature; an example is the case of demand response applications, which rely on forecasts and where user perception may impede uptake of flexible pricing and participation in such demand response schemes. We will not provide any further technical detail here, as our purpose in this paper is not the AI energy forecasting itself but the classification of its particular development along the TRL scale. At the project's outset, we started from a TRL 2, having a clear formulation of the technology concept that we would seek to develop and validate in real settings. Our validation was done in a real building--one that could be considered to be between'relevant' (TRL 6) and 'operational' (TRL 7). Depending on the application context, it may sometimes be difficult to draw a clear line between these two levels. Our interpretation was that a truly operational environment would present more data collection uncertainty than the one in which we tried our algorithm, in which data issues were already streamlined, and which presented no real difficulties. Thus, an operational environment might present some further data challenges, which we were, in this case, not exposed to. However, the question that soon arose was not so much whether the achieved TRL was 6 or 7. Instead, the questions pertinent to our discussion here were: how one can climb further up the ladder, reaching TRL 8 and 9. What can TRL 8 mean in our case? How can you qualify a forecasting algorithm? Even more, how can you possibly generate a market model around a fully-developed system when the system is a forecasting algorithm? The answer is, simply, that you cannot. It is highly unlikely that one will find in the market such a technology, per se. Such a development may indeed generate some new value but will never amount to a self-standing market value. We would also argue that this is hardly a rare case. On the contrary, it is quite typical that a new technology presents some innovative features but falls short of enabling, by itself, any viable business case. At that point, one may, of course, consider traditional licensing instruments and arrangements. However, if one wishes to remain on the maturity upgrading path, this is not a viable option. Licensing may provide some financial benefit, but it will take you off the TRL ladder for good. If you wish to push your way upwards on the TRL ladder, licensing is obviously not a solution. Indeed, TRLs have been designed for endeavors strategically looking forward to reaching the highest level. Licensing may be a way out of this aspiration, but what happens if it is not possible or desirable? We would argue that co-creation is, then, the only option left for our technology endeavor. Through co-creation, we effectively acknowledge that our algorithm is not a carrier of standalone market value and that a multistakeholder environment is due, one where our specific development would be an integral part of a broader solution, of which it might increase the market value.