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Zero-Shot Solar Power Efficiency Prediction Method Considering PCC-Based Climate Similarity
Dongjun Kim, Sungwoo Park, Jaeuk Moon, Eenjun Hwang
http://doi.org/10.5626/JOK.2023.50.7.581
Thermal power generation is a power generation method that occupies a large proportion in Korea and abroad due to its low unit price. However, due to its disadvantage of emitting large amounts of harmful substances that can cause health and environmental problems, renewable energy is in the spotlight as an alternative power source. Among various renewable energy generation methods, solar power generation is receiving the most attention because of its advantages such as ease in maintenance. Various solar power generation forecasting studies are being conducted to improve the uncertainty of volatile solar power generation and ensure stability in power supply. However, existing studies have limitations in that they are only applicable when there is a sufficient amount of historical power generation data. Therefore, this paper proposes a solar power generation efficiency prediction method based on zero-shot learning that utilizes historical data of similar regions by concerning weather similarity to solve the cold-start problem, a problem that occurs in prediction when historical data in the target region are lacking. Comparison results revealed that the proposed method had better performance overall in the target area, with a one-hour-based method showing the best prediction performance among other criteria.
A Weight-based Multi-domain Recommendation System for Alleviating the Cold-Start Problem
http://doi.org/10.5626/JOK.2021.48.10.1090
A recommendation system predicts users’ ratings based on users’ past behaviors and item preferences. One of the most famous types of recommendation systems is the collaborative filtering method that predicts users’ ratings based on the rating information from users with similar preferences. In order to predict the preferences of users, we need adequate information about users’ interactive information on items. Otherwise, it is very difficult to make accurate predictions for users without adequate information. This phenomenon is called the cold-start problem. In this paper, we propose a multi-domain recommendation system that utilizes the rating information of multiple domains. We propose a method that calculates the weight of each auxiliary domain to maximize the confidence of predicted ratings from multiple auxiliary domains and verify the performance of the proposed method through extensive experiments. As a result, we demonstrate that our algorithm produces better recommendation results compared to the classical algorithms simply utilized in multiple domain settings.
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