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Title: Load and renewable energy forecasting for a microgrid using persistence technique
Authors: Dutta, Shreya
Li, Yanling
Venkataraman, Aditya
Costa, Luis M.
Jiang, Tianxiang
Plana, Robert
Tordjman, Philippe
Choo, Fook Hoong
Foo, Chek Fok
Püttgen, Hans Björn
Keywords: Load Forecasting
Renewable Energy Forecasting
DRNTU::Engineering::Environmental engineering
Issue Date: 2017
Source: Dutta, S., Li, Y., Venkataraman, A., Costa, L. M., Jiang, T., Plana, R., . . . Püttgen, H. B. (2017). Load and renewable energy forecasting for a microgrid using persistence technique. Energy Procedia, 143, 617-622. doi:10.1016/j.egypro.2017.12.736
Series/Report no.: Energy Procedia
Abstract: A microgrid system, be it connected to the utility grid or an independent system, usually consists of a mix of generation - renewable and non-renewable; loads - controllable or non-controllable and Energy Storage Systems (ESSs) such as batteries or flywheels. In order to determine how much power is utilized from the controllable resources such as ESS, diesel generators, micro-turbines or gas turbines, we first need to determine how much the demand is or how much the renewable energy sources are generating is which is accomplished using forecasting techniques. Due to the intermittent nature of renewable resources such as wind energy or solar energy, it is difficult to forecast wind power or solar power accurately. These forecasts are highly dependent on weather forecasts. It is evident that forecast of any data based on forecast of other parameters would lead to further inaccuracy, even if the relation between the inputs and output maybe predetermined through regression methods. Therefore, this paper illustrates an approach to use historical power data instead of numerical weather predictions to produce short-term forecast results. The concept is based on persistence method presented in [1]. This method uses the “today equals tomorrow” concept. From [2], we know that persistence technique produces results that are more accurate as compared to other forecasting techniques for a look-ahead time of 4-6 hours. Both [1] and [2] were based on wind power forecasting. In this paper, we investigate persistence method for short-term electrical demand, solar PV (Photovoltaic) power and wind power forecasting. Since the forecasts are dependent on historical averages of the data in the ‘near’ past, the accuracy is inversely proportional to the variation of power between the historical data and the actual data.
ISSN: 1876-6102
DOI: 10.1016/j.egypro.2017.12.736
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Journal Articles

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