Dear CEMA 2020-2021 team,
My name is Irina Chuchueva. I applied to your conference, and my paper was rejected.
I am making an official complaint. I claim that my paper was rejected unfairly and with bias. The details are below.
On February 22, 2021, I submitted my paper “The Short-term Electricity Consumption Forecast Competition Under COVID-19 Lockdown Conditions.” The paper was published on my website on June 8, 2021.
On April 27, 2021, I got my rejection notification:
Dear Ms. Chuchueva,
We have received the report indicated below. Regretfully, your paper cannot be accepted for presentation at CEMA 2021.
Andrea Roncoroni and Juan Ignacio Peña
This paper puts forward a number of interesting issues of practical use when forecasting day-ahead electricity consumption. The analysis is based on models implemented in proprietary software. The paper does not provide a self-contained description of any of the cited models. In particular, none of the results can be reproduced by the reader. In conclusion, although the paper might have some merits, it looks more like a technical report for the industry than an academic production. As such, we think it is not suitable for presentation at an academic conference.
On April 27, the same date the rejection came, I paid 75 euros to listen to the accepted ones. I participated in and listened to 15 presentations, four of which were related to forecasting. I published detailed notes about these four presentations, one by one.
Basically, my paper was rejected by two main criterias: non-reproducible and not scientific enough. I noted that all forecasting studies presented at the conference were neither reproducible nor practical. First, to be reproducible you need to publish both data and code. None of that was done by researchers, and no explicit reference to any non-disclosure agreement was made. Second, to be practical, you need to prove high efficiency not against some benchmark model, developed by the same researcher, but against the existing production solution. None of the offered models have been proved to be efficient in such a respect. Although I can’t judge the quality of the text because I don’t have access to it, the presentations shed some light on the matter.
At the same time, the forecast model I presented in my paper “The Short-term Electricity Consumption Forecast Competition Under COVID-19 Lockdown Conditions” is a core of the production software. The model was patented by its intellectual right owner, AnalyticsHub LLC. The model is proved to be the most efficient in the country and has won a number of forecast competitions. My paper is also highly contemporary and related to short-term forecasting during a time interval with high uncertainty — the beginning of COVID-19 lockdown. And finally, I publish neither code nor other competitor results, but I explicitly refer to the non-disclosure agreement signed by AnalyticsHub LLC and me as its software developer.
Based on that, I have evidence that my paper was rejected with bias. I demand an investigation of the matter. The text of this complaint is openly published. Please note that the further evolution of my complaint will be published as well.