Designing a short-term load forecasting model in the urban smart grid system

Chen Li

Applied Energy, 2020, vol. 266, issue C, No S0306261920303627

Abstract: The transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which provides a cleaner energy source to the urban smart grid system. However, owing to the volatility and intermittency of RE, it is challenging to design an accurate and reliable short-term load forecasting model. Recently, machine learning (ML) based forecasting models have been applied for short-term load forecasting whereas most of them ignore the importance of characteristics mining, parameters fine-tuning, and forecasting stability. To dissolve the above issues, a short-term load forecasting model is proposed that incorporates thorough data mining and multi-step rolling forecasting. To alleviate the chaos of short-term load, a de-noising method based on decomposition and reconstruction is used. Then, a phase space reconstruction (PSR) method is employed to dynamically determine the train-test ratios and neurons settings of the artificial neural network (ANN). Further, a multi-objective grasshopper optimization algorithm (MOGOA) is applied to optimize the parameters of ANNs. Case studies are conducted in the urban smart grid systems of Victoria and New South Wales in Australia. Simulation results show that the proposed model can forecast short-term load well with various measurement metrics. Multiple criterion and statistical evaluation also show the good performance of the proposed forecasting model in terms of accuracy and stability. To conclude, the proposed model achieves high accuracy and robustness, which will provide references to RE transitions and smart grid optimization, and offer guidance to sustainable city development.

Keywords: Smart grid; Short-term load forecasting; Neural networks; Multi-objective optimization algorithm; Urban sustainability (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (12)

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DOI: 10.1016/j.apenergy.2020.114850

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