BiLSTM 기반의 설명 가능한 태양광 발전량 예측 기법
Explainable Photovoltaic Power Forecasting Scheme Using BiLSTM
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초록
최근 화석연료의 무분별한 사용으로 인한 자원고갈 문제 및 기후변화 문제 등이 심각해짐에 따라 화석연료를 대체할 수 있는 신재생에너지에대한 관심이 증가하고 있다. 특히 신재생에너지 중 태양광 에너지는 다른 신재생에너지원에 비해 고갈될 염려가 적고, 공간적인 제약이 크지 않아전국적으로 수요가 증가하고 있다. 태양광 발전 시스템에서 생산된 전력을 효율적으로 사용하기 위해서는 보다 정확한 태양광 발전량 예측 모델이필요하다. 이를 위하여 다양한 기계학습 및 심층학습 기반의 태양광 발전량 예측 모델이 제안되었지만, 심층학습 기반의 예측 모델은 모델 내부에서일어나는 의사결정 과정을 해석하기가 어렵다는 단점을 보유하고 있다. 이러한 문제를 해결하기 위하여 설명 가능한 인공지능 기술이 많은 주목을받고 있다. 설명 가능한 인공지능 기술을 통하여 예측 모델의 결과 도출 과정을 해석할 수 있다면 모델의 신뢰성을 확보할 수 있을 뿐만 아니라해석된 도출 결과를 바탕으로 모델을 개선하여 성능 향상을 기대할 수도 있다. 이에 본 논문에서는 BiLSTM(Bidirectional Long Short-Term Memory)을 사용하여 모델을 구성하고, 모델에서 어떻게 예측값이 도출되었는지를 SHAP(SHapley Additive exPlanations)을 통하여 설명하는 설명 가능한태양광 발전량 예측 기법을 제안한다.
Abstract
Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generationhas become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels isincreasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to otherenergy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by thephotovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machinelearning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms ofinterpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting resultsare derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability ofthe model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increasethe forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecastingscheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).
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