Research Conducted at COMSATS University Islamabad Has Updated Our Knowledge about Information and Data Preprocessing (Electric Load Forecasting Based On Deep Learning and Optimized By Heuristic Algorithm In Smart Grid)
- Jul 9, 2020 3:16 pm GMT
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2020 JUL 08 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- Investigators publish new report on Information Technology - Information and Data Preprocessing. According to news reporting originating from Islamabad, Pakistan, by NewsRx correspondents, research stated, “Accurate electric load forecasting is important due to its application in the decision making and operation of the power grid. However, the electric load profile is a complex signal due to the non-linear and stochastic behavior of consumers.”
Our news editors obtained a quote from the research from COMSATS University Islamabad, “Despite much research conducted in this area; still, accurate forecasting models are needed. In this article, a novel hybrid short-term electric load forecasting model is proposed. The proposed model is an integrated framework of data pre-processing and feature selection module, training and forecasting module, and an optimization module. The data pre-processing and feature selection module is based on modified mutual information (MMI) technique, which is an improved version of the mutual information technique, used to select abstractive features from historical data. The training and forecasting module is based on factored conditional restricted Boltzmann machine (FCRBM), which is a deep learning model, empowered via learning to forecast the future electric load. The optimization module is based on our proposed genetic wind-driven (GWDO) optimization algorithm, which is used to fine-tune the adjustable parameters of the model. The accuracy of the proposed framework is evaluated through historical hourly load data of three USA power grids, taken from publicly available PJM electricity market.”
According to the news editors, the research concluded: “The proposed model is validated by comparing it with four recent forecasting models like Bi-level, mutual information-based artificial neural network (MI-ANN), ANN-based accurate and fast converging (AFC-ANN), and long short-term memory (LSTM) in terms of accuracy and convergence rate.”
For more information on this research see: Electric Load Forecasting Based On Deep Learning and Optimized By Heuristic Algorithm In Smart Grid. Applied Energy, 2020;269():114915. Applied Energy can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Applied Energy - http://www.journals.elsevier.com/applied-energy/)
The news editors report that additional information may be obtained by contacting G. Hafeez, COMSATS University Islamabad, Islamabad 44000, Pakistan. Additional authors for this research include K.S. Alimgeer and I. Khan.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.apenergy.2020.114915. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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