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IEEE access : practical research, open solutions, v.7, 2019년, pp.82584 - 82598
Motepe, Sibonelo (Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa) , Hasan, Ali N. (Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa) , Stopforth, Riaan (Stopforth Mechatronics, Robotics and Research Lab, School of Engineering, University of Kwa-Zulu Natal, Durban, South Africa)
AI-Helper
Load forecasting is useful for various applications, including maintenance planning. The study of load forecasting using recent state-of-the-art hybrid artificial intelligence (AI) and deep learning (DL) techniques is limited in South Africa (SA) and South African power distribution networks. This p...
Proc Int Conf Ind Commercial Use of Energy (ICUE) Forecasting net energy consumption of South Africa using artificial neural network tartibu 2018 1
10.1109/JEEIT.2019.8717434
10.1109/IEEEGCC.2017.8448125
10.1109/ICCRE.2017.7935064
Dehghanian, Payman, Zhang, Bei, Dokic, Tatjana, Kezunovic, Mladen. Predictive Risk Analytics for Weather-Resilient Operation of Electric Power Systems. IEEE transactions on sustainable energy, vol.10, no.1, 3-15.
Yu, Zeyuan, Niu, Zhewen, Tang, Wenhu, Wu, Qinghua. Deep Learning for Daily Peak Load Forecasting–A Novel Gated Recurrent Neural Network Combining Dynamic Time Warping. IEEE access : practical research, open solutions, vol.7, 17184-17194.
10.1109/PEDES.2018.8707876
10.1109/POWERCON.2010.5666060
Proc 45th Int Univ Power Eng Conf (UPEC) Application of adaptive neuro fuzzy inference system (ANFIS) based short term load forecasting in south african power networks yuill 2010 1
10.1109/AFRCON.1999.821906
10.1109/SMC.2017.8123012
Martín-Doñas, Juan Manuel, Gomez, Angel Manuel, Gonzalez, Jose A., Peinado, Antonio M.. A Deep Learning Loss Function Based on the Perceptual Evaluation of the Speech Quality. IEEE signal processing letters, vol.25, no.11, 1680-1684.
arXiv 1702 01923 Comparative study of CNN and RNN for natural language processing yin 2017
mashao 2019
holloway 2019
10.1109/IJCNN.2017.7966398
10.1109/IJCNN.2017.7966378
10.1109/ICCNC.2017.7876196
Nguyen, Ngoc-Quang, Lee, Sang-Woong. Robust Boundary Segmentation in Medical Images Using a Consecutive Deep Encoder-Decoder Network. IEEE access : practical research, open solutions, vol.7, 33795-33808.
Xue-Wen Chen, Xiaotong Lin. Big Data Deep Learning: Challenges and Perspectives. IEEE access : practical research, open solutions, vol.2, 514-525.
Albarqouni, Shadi, Baur, Christoph, Achilles, Felix, Belagiannis, Vasileios, Demirci, Stefanie, Navab, Nassir. AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images. IEEE transactions on medical imaging, vol.35, no.5, 1313-1321.
Majumder, Navonil, Poria, Soujanya, Gelbukh, Alexander, Cambria, Erik. Deep Learning-Based Document Modeling for Personality Detection from Text. IEEE intelligent systems, vol.32, no.2, 74-79.
10.1109/IREC.2018.8362552
10.1109/IREC.2017.7926047
Hochreiter, Sepp, Schmidhuber, Jürgen. Long Short-Term Memory. Neural computation, vol.9, no.8, 1735-1780.
Forecasting electricity demand in South Africa using artificial intelligence marwala 2015
Xia Chen, Zhao Yang Dong, Ke Meng, Yan Xu, Kit Po Wong, Ngan, H. W.. Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping. IEEE transactions on power systems : a publication of the Power Engineering Society, vol.27, no.4, 2055-2062.
10.1109/IJCNN.2014.6889651
Huang, Guang-Bin, Zhu, Qin-Yu, Siew, Chee-Kheong. Extreme learning machine: Theory and applications. Neurocomputing, vol.70, no.1, 489-501.
Miche, Y., Sorjamaa, A., Lendasse, A.. OP-ELM: Theory, Experiments and a Toolbox. Lecture notes in computer science, vol.5163, 145-154.
10.1109/RoboMech.2017.8261137
Parallel Distributed Processing Explorations in the Microstructure of Cognition Psychological and Biological Models mcclelland 1986 2
Ceperic, Ervin, Ceperic, Vladimir, Baric, Adrijan. A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines. IEEE transactions on power systems : a publication of the Power Engineering Society, vol.28, no.4, 4356-4364.
10.1007/0-387-23471-3_12
10.1109/SMC.2013.383
Kong, Weicong, Dong, Zhao Yang, Hill, David J., Luo, Fengji, Xu, Yan. Short-Term Residential Load Forecasting Based on Resident Behaviour Learning. IEEE transactions on power systems : a publication of the Power Engineering Society, vol.33, no.1, 1087-1088.
Welikala, Shirantha, Dinesh, Chinthaka, Ekanayake, Mervyn Parakrama B., Godaliyadda, Roshan Indika, Ekanayake, Janaka. Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting. IEEE transactions on smart grid, vol.10, no.1, 448-461.
Kong, Weicong, Dong, Zhao Yang, Jia, Youwei, Hill, David J., Xu, Yan, Zhang, Yuan. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE transactions on smart grid, vol.10, no.1, 841-851.
10.1109/ICDMAI.2017.8073480
Liu, Di, Sun, Yi, Qu, Yao, Li, Bin, Xu, Yonghai. Analysis and Accurate Prediction of User’s Response Behavior in Incentive-Based Demand Response. IEEE access : practical research, open solutions, vol.7, 3170-3180.
Zeng, Bo, Wei, Xuan, Zhao, Dongbo, Singh, Chanan, Zhang, Jianhua. Hybrid probabilistic-possibilistic approach for capacity credit evaluation of demand response considering both exogenous and endogenous uncertainties. Applied energy, vol.229, 186-200.
Proc Power Energy Soc Gen Meet (PESGM) Forecast load impact from demand response resources zhou 2016 1
Han, Lingyi, Peng, Yuexing, Li, Yonghui, Yong, Binbin, Zhou, Qingguo, Shu, Lei. Enhanced Deep Networks for Short-Term and Medium-Term Load Forecasting. IEEE access : practical research, open solutions, vol.7, 4045-4055.
Kanagawa, M., Nakata, T.. Assessment of access to electricity and the socio-economic impacts in rural areas of developing countries. Energy policy, vol.36, no.6, 2016-2029.
Morden Energy for All 2018
Doll, Christopher N.H., Pachauri, Shonali. Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery. Energy policy, vol.38, no.10, 5661-5670.
Winkler, H., Simoes, A.F., Rovere, E.L.l., Alam, M., Rahman, A., Mwakasonda, S.. Access and Affordability of Electricity in Developing Countries. World development, vol.39, no.6, 1037-1050.
Integrated National Electrification Program (INEP) government 2019
prasad 2019
10.1109/INES.2011.5954729
10.1109/IJCNN.2014.6889898
Block, H. D.. The Perceptron: A Model for Brain Functioning. I. Reviews of modern physics, vol.34, no.1, 123-135.
Tso, G.K.F., Yau, K.K.W.. Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy : technologies, resources, reserves, demands, impact, conservation, management, policy, vol.32, no.9, 1761-1768.
Proc IEEE Int Conf Comput Tool (EUROCON) Improving single classifiers prediction accuracy for underground water pump station in a gold mine using ensemble techniques ali 2015 1
10.1109/CIES.2013.6611745
Motepe, Sibonelo, Twala, Bhekisipho, Wang, Qing-Guo, Stopforth, Riaan. Determining Distribution Power System Loading Measurements Accuracy Using Fuzzy Logic. Procedia manufacturing, vol.7, 435-439.
Artificial Intelligence A Guide to Intelligent Systems negnevitsky 2005
Rejc, M., Pantos, M.. Short-Term Transmission-Loss Forecast for the Slovenian Transmission Power System Based on a Fuzzy-Logic Decision Approach. IEEE transactions on power systems : a publication of the Power Engineering Society, vol.26, no.3, 1511-1521.
10.1007/978-3-662-43505-2
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