Application of time-series and Artificial Neural Network models in short term load forecasting for scheduling of storage devices

Proceedings of the Universities Power Engineering Conference

Volumn , Issue , 2014, Pages

  Ahmed, K M U ^a   Ampatzis, M ^a   Nguyen, P H ^a   Kling, W L ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Artificial Neural Network (ANN); AutoRegressive Integrated Moving Average (ARIMA); machine learning; short term load forecasting (STLF); storage device scheduling

Indexed keywords

AUTOREGRESSIVE MOVING AVERAGE MODEL; ELECTRIC ENERGY STORAGE; ELECTRIC POWER PLANT LOADS; ELECTRIC POWER UTILIZATION; ENERGY RESOURCES; FORECASTING; LEARNING SYSTEMS; SCHEDULING; SMART POWER GRIDS;

ARTIFICIAL NEURAL NETWORK MODELS; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS; DEVICE SCHEDULING; DISTRIBUTED ENERGY RESOURCES; ELECTRICITY CONSUMPTION FORECASTING; INTEGRATED ENERGY STORAGES; SHORT TERM LOAD FORECASTING;

NEURAL NETWORKS;

EID: 84910070296     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/UPEC.2014.6934761     Document Type: Conference Paper

References (17)

1. R. Sadownik and E. Barbosa, "Short-Term Forecasting of Industrial Electrical Consumption in Brazil", Journal of Forecasting, vol. 18, no. 3, pp. 215-224, 1999.
2. D. Park, M. El Sharkawi, I. Marks, L. Atlas and M. Damborg, "Electric Load Forecasting Using An Artificial Neural Network", IEEE Transaction on Power System, vol. 6(2), pp. 442-449, 1991.
3. H. NIE, G. LIU, X. LIU and Y. WANG, "Hybrid of ARIMA and SVMs for Short-Term Load Forecasting", International Conference on Future Energy, Environment and Materials, vol. 16, pp. 1455-1460, 2012.
4. G. Zhang, "Time series forecasting using a hybrid ARIMA and neural network model", Neurocomputing, Elsevier, vol. 50, pp. 159-175, 2003.
5. S. Sargunaraj, D. Gupta and S. Devi, "Short-Term Load Forecasting for Demand Side Managment", in IEEE Proceedings-Generation, Transmission, 1997.
6. Z. Yu, "A Temperature Match Based Optimization Method for Daily Load Prediction Considering DLC Effect", in IEEE Transactions on Power Systems, 1996.
7. Y. C. Li, T. J. Fang and E. K. Yu, "Study Of Support Vector Machines For Short-Term Load Forecasting", Proceedings of the CSEE, vol 26, no. 2, pp. 10-13, 2003.
8. F. Grimaccia, M. Mussetta and R. Zich, "Advanced Predictive Models towards PV Energy Integration In Smart Grid", IEEE International Conference on Fuzzy Systems, pp. 1-6, 2012.
9. D. Bunn, "Forecasting Loads and Prices in Competitive Power Markets", Proceedings of the IEEE, vol. 88, no. 2, pp. 163-169, 2000.
10. K. Hipel and A. McLeod, "Time Series Modelling of Water Resources and Environment System", in Elsevier, Amsterdam, 1994.
11. G. E. P. Box and G. Jenkins, "Time Series Analysis, Forecasting and Control", San Francisco, CA: Holden-Day, 1970.
12. K. Ho, Y. Hsu and C. Yuan", Short Term Load Forecasting Using A Multilayer Neural Network With An Adaptive Learning Algorithom", IEEE Transation on Power System, vol. 7(1), pp. 259-266, 1992.
13. B. M. J. Vonk, P. H. Nguyen, M. O. W. Grond, I. G. Slootweg and W. L. Kling, "Improving Short-term Load Forecasting for a Local Energy StorageSystem", in 47International Universities Power Engineering Conference (UPEC), London, 2012.
14. G. Zhang, E. B. Patuwo and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art", International Journal of Forecasting, vol. 14, no. 1, pp. 35-62, 1998.
15. C. Hamzaçebi, "Improving artificial neural networks performance in seasonal time series forecasting", Information Sciences: Genetic and Evolutionary Computing, vol. 178, no. 23, p. 4550-4559, 2008.
16. B. Asare-Bediako, W. L. Kling and P. Ribeiro, "Day-Ahead Residential Load Forecasting with Artificial Neural Networks using Smart Meter Data", PowerTech, pp. 1-6, 2013.
17. M. B. Tasre, V. N. Ghate and P. P. Bedekar, "Hourly Load Forecasting Using Artificial Neural Network for a Small Area", International Conference On Advances In Engineering, Science And Management, pp. 379-385, 2012.