KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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A Multilayer Perceptron-Based Electric Load Forecasting Scheme via Effective Recovering Missing Data

효과적인 결측치 보완을 통한 다층 퍼셉트론 기반의 전력수요 예측 기법

  • 문지훈 (고려대학교 전기전자공학과) ;
  • 박성우 (고려대학교 전기전자공학과) ;
  • 황인준 (고려대학교 전기전자공학과)
  • Received : 2018.10.05
  • Accepted : 2018.10.29
  • Published : 2019.02.28
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Abstract

Accurate electric load forecasting is very important in the efficient operation of the smart grid. Recently, due to the development of IT technology, many works for constructing accurate forecasting models have been developed based on big data processing using artificial intelligence techniques. These forecasting models usually utilize external factors such as temperature, humidity and historical electric load as independent variables. However, due to diverse internal and external factors, historical electrical load contains many missing data, which makes it very difficult to construct an accurate forecasting model. To solve this problem, in this paper, we propose a random forest-based missing data recovery scheme and construct an electric load forecasting model based on multilayer perceptron using the estimated values of missing data and external factors. We demonstrate the performance of our proposed scheme via various experiments.

정확한 전력수요 예측은 스마트 그리드의 효율적인 운영에 있어 매우 중요하다. 최근 IT 기술이 획기적으로 발전되면서, 인공지능 기법을 이용한 빅 데이터 처리를 기반으로 정확한 전력수요를 예측하는 많은 연구가 진행되고 있다. 이러한 예측 모델은 주로 외부 요인과 과거 전력수요를 독립 변수로 사용한다. 하지만, 다양한 내부적 또는 외부적 원인으로 전력수요 데이터의 결측치가 발생하게 되면 정확한 예측 모델을 구성하기가 어렵다. 이에 본 논문에서는 랜덤 포레스트 기반의 결측치 데이터 보완 기법을 제안하고, 보완된 데이터를 기반으로 한 다층 퍼셉트론 기반의 전력수요 예측 모델을 구성한다. 다양한 실험을 통해 제안된 기법의 예측 성능을 입증한다.

Keywords

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Fig. 1. Our Framework for Short-Term Load Forecasting

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Fig. 2. Architecture of Multilayer Perceptron for Our Forecasting Model

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Fig. 3. Feature Importances in Random Forest

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Fig. 4. Recovering Missing Values Using the Random Forest

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Fig. 5. Short-Term Electric Load Forecasting

Table 1. Season & Time-Period Classification

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Table 3. 10-Fold Cross Validation

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Table 4. Split Data into Training and Test Set

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Table 5. RMSE(MAE) Results with Multilayer Perceptron

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Table 6. RMSE(MAE) Distribution for Each Model

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Table 2. SVR/ANN Model Configuration

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