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http://dx.doi.org/10.3745/KTSDE.2019.8.2.67

A Multilayer Perceptron-Based Electric Load Forecasting Scheme via Effective Recovering Missing Data  

Moon, Jihoon (고려대학교 전기전자공학과)
Park, Sungwoo (고려대학교 전기전자공학과)
Hwang, Eenjun (고려대학교 전기전자공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.8, no.2, 2019 , pp. 67-78 More about this Journal
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.
Keywords
Smart Grid; Electric Load Forecasting; Missing Data Handling; Deep Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 S. Ryu, J. Noh, and H. Kim, "Deep Neural Network Based Demand Side Short Term Load Forecasting," Energies, Vol.10, No.1, pp.1-20, 2017.   DOI
2 T. Niet et al., "Hedging the risk of increased emissions in long term energy planning," Energy Strategy Reviews, Vol.16, pp.1-12, 2017.   DOI
3 Y.-M. Wi, S. Kong, J. Lee, and S.-K. Joo, "Demand-Side Management Program Planning Using Stochastic Load Forecasting with Extreme Value Theory," Journal of Electrical Engineering & Technology, Vol.11, No.5, pp.1093-1099, 2016.   DOI
4 T. Hong and S. Fan, "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Vol.32, No.3, pp.914-938, 2016.   DOI
5 X. Ke, A. Jiang, and N. Lu, "Load Profile Analysis and Short-term Building Load Forecast for a University Campus," in Proceedings of the IEEE Power & Energy Society General Meeting, Boston, pp.1-5, 2016.
6 B. Zhang, J.-L. Wu, and P.-C. Chang, "A multiple time series-based recurrent neural network for short-term load forecasting," Soft Computing, Vol.22, No.12, pp.4099-4112, 2018.   DOI
7 S. Park, J. Moon, and E. Hwang, "A Comparison of Various Machine Learning Techniques for Missing Values Recovering in Load Prediction," in Proceedings of the KSIC Spring Conference, Busan, pp.1-2, 2018.
8 J. Moon, S. Jun, J. Park, Y.-H. Choi, and E. Hwang, "An Electric Load Forecasting Scheme for University Campus Buildings Using Artificial Neural Network and Support Vector Regression," KIPS Transactions on Computer and Communication Systems, Vol.5, No.10, pp.293-302, 2016.   DOI
9 W.-W. Kim, J.-S. Shin, and J.-O. Kim, "Operation Strategy of Multi-Energy Storage System for Ancillary Services," IEEE Transactions on Power Systems, Vol.32, No.6, pp.4409-4417, 2017.   DOI
10 J. Park, J. Moon, and E. Hwang, "An Electric Load Forecasting Scheme with High Time Resolution Based on Artificial Neural Network," KIPS Transactions on Software and Data Engineering, Vol.6, No.11, pp.527-536, 2017.   DOI
11 B. Yildiz, J. I. Bilbao, and A. B. Sproul, "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Vol.73, pp.1104-1122, 2017.   DOI
12 M. R. Sarkar, M. G. Rabbani, A. R. Khan, and M. M. Hossain, "Electricity Demand Forecasting of Rajshahi City in Bangladesh Using Fuzzy Linear Regression Model," in Proceedings of the International Conference on Electrical Engineering and Information Communication Technology, Dhaka, pp.1-3, 2015.
13 M. Q. Raza and A. Khosravi, "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Vol.50, pp.1352-1372, 2015.   DOI
14 L. Hernandez et al., "A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings," IEEE Communications Surveys & Tutorials, Vol.16, No.3, pp.1460-1495, 2014.   DOI
15 A. Almalaq and G. Edwards, "A Review of Deep Learning Methods Applied on Load Forecasting," in Proceedings of the IEEE International Conference on Machine Learning and Applications, Cancun, pp.511-516, 2017.
16 G. Dudek, "Pattern-based local linear regression models for short-term load forecasting," Electric Power System Research, Vol.130, pp.139-147, 2016.   DOI
17 A. Gerossier, R. Girard, G. Kariniotakis, and A. Michiorri, "Probabilistic Day-Ahead Forecasting of Household Electricity Demand," CIRED - Open Access Proceedings Journal, Vol.2017, No.1, pp.2500-2504, 2017.   DOI
18 K. Grolinger, A. L'Heureux, M. A. M. Capretz, and L. Seewald, "Energy Forecasting for Event Venues: Big Data and Prediction Accuracy," Energy and Buildings, Vol.112, pp.222-233, 2016.   DOI
19 R. K. Jain, K. M. Smith, P. J. Culligan, and J. E. Taylor, "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Vol.123, pp.168-178, 2014.   DOI
20 L. Ghelardoni, A. Ghio, and D. Anguita, "Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression," IEEE Transactions on Smart Grid, Vol.4, No.1, pp.549-556, 2013.   DOI
21 S. Jurado, A. Nebot, F. Mugica, and N. Avellana, "Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques," Energy, Vol.86, pp.276-291, 2015.   DOI
22 A. Bagnasco, F. Fresi, M. Saviozzi, F. Silvestro, and A. Vinci, "Electrical consumption forecasting in hospital facilities: An application case," Energy and Buildings, Vol.103, pp.261-270, 2015.   DOI
23 N. Zeng, H. Zhang, W. Liu, J. Liang, and F. E. Alsaadi, "A switching delayed PSO optimized extreme learning machine for short-term load forecasting," Neurocomputing, Vol.240, pp.175-182, 2017.   DOI
24 S. S. Reddy, "Bat algorithm-based back propagation approach for short-term load forecasting considering weather factors," Electrical Engineering, Vol.100, No.3, pp.1297-1303, 2018.   DOI
25 M. Abdel-Nasser and K. Mahmoud, "Accurate photovoltaic power forecasting models using deep LSTM-RNN," Neural Computing and Applications, pp.1-14, 2017.
26 M. W. Ahmad, M. Mourshed, and Y. Rezgui, "Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption," Energy and Buildings, Vol.147, pp.77-89, 2017.   DOI
27 A. S. Ahmad et al., "A review on applications of ANN and SVM for building electrical energy consumption forecasting," Renewable and Sustainable Energy Reviews, Vol.33, pp.102-109, 2014.   DOI
28 W. Kong et al., "Short-Term Residential Load Forecasting based on LSTM Recurrent Neural Network," IEEE Transactions on Smart Grid, pp.1-11, 2017.
29 P.-H. Kuo and C.-J. Huang, "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, Vol.11, No.1, pp.1-13, 2018.   DOI
30 F. Tang and H. Ishwaran, "Random forest missing data algorithms," Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol.10, No.6, pp.363-377, 2017.   DOI
31 K.-H. Kim, R.-J. Park, S.-W. Jo, and K.-B. Song, "24-Hour Load Forecasting Algorithm Using Artificial Neural Network in Summer Weekdays," Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, Vol.31, No.12, pp.113-119, 2017.   DOI
32 G. Panchal, A. Ganatra, Y. P. Kosta, and D. Panchal, "Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers," International Journal of Computer Theory and Engineering, Vol.3, No.2, pp.332-337, 2011.
33 F. Pedregosa et al., "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, Vol.12, pp.2825-2830, 2011.
34 G. Hinton, N. Srivastava, and K. Swersky, Lecture 6aoverview of mini-batch gradient descent [Internet], https://class.coursera.org/neuralnets-2012-001/lecture.
35 J. Niu, J. Chen, and Y. Xu, "Twin support vector regression with Huber loss," Journal of Intelligent and Fuzzy Systems, Vol.32, No.6, pp.4247-4258, 2017.   DOI
36 T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, "How Many Trees in a Random Forest?" in International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer, Berlin, Heidelberg, pp.154-168, 2012.
37 W. Lee, J. Jung, and M. Lee, "Development of 24-hour Optimal Scheduling Algorithm for Energy Storage System using Load Forecasting and Renewable Energy Forecasting," in Proceedings of the IEEE Power & Energy Society General Meeting, Chicago, pp.1-5, 2017.
38 C. Fan, F. Xiao, S. Y. Hea, and Y. Zhao, "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Vol.195, pp.222-233, 2017.   DOI
39 A. Dedinec, S. Filiposka, A. Dedinec, and L. Kocarev, "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Vol.115, pp.1688-1700, 2016.   DOI
40 H. Chitsaz, H. Shaker, H. Zareipour, D. Wood, and N. Amjady, "Short-term electricity load forecasting of building in microgrids," Energy and Buildings, Vol.99, pp.50-60, 2015.   DOI
41 J. Moon, K.-H. Kim, and E. Hwang, "A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics," in Proceedings of the IEEE International Conference on Big Data and Smart Computing, Shanghai, pp.219-226, 2018.
42 M. Abadi et al., "Tensorflow: a system for large-scale machine learning," OSDI, Vol.16. pp.265-283, 2016.