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Energies 2019, 12(1), 164;

Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach

School of Electrical Engineering and Computing, The University of Newcastle, Callaghan 2308, Australia
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE
Author to whom correspondence should be addressed.
Received: 11 November 2018 / Revised: 27 December 2018 / Accepted: 1 January 2019 / Published: 4 January 2019
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
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Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability. View Full-Text
Keywords: artificial neural network; load prediction; smart grid; heuristic optimization; energy trade; accuracy artificial neural network; load prediction; smart grid; heuristic optimization; energy trade; accuracy

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Ahmad, A.; Javaid, N.; Mateen, A.; Awais, M.; Khan, Z.A. Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach. Energies 2019, 12, 164.

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