Abstract
This paper proposes an improved interval fuzzy modeling (imIFML) technique based on modified linear programming and actual boundary points of data. The imIFML technique comprises four design stages. The first stage is based on conventional interval fuzzy modeling (coIFML) with first-order model and linear programming. The second stage defines reference lower and upper bounds of data using MATLAB. The third stage initially adjusts scaling parameters in the modified linear programming. The last stage automatically fine-tunes parameters in the modified linear programming to realize the best possible model. Lower and upper bounds approximated by the imIFML technique are closely fitted to the reference lower and upper bounds, respectively. The proposed imIFML is thus significantly less conservative in cases of large variation in data, while robustness is inherited from the coIFML. Design flowcharts, equations, and sample MATLAB code are presented for reference in future experiments. Performance and efficacy of the introduced imIFML are evaluated to estimate solar photovoltaic, wind and battery power in a demonstrative renewable energy system under large data changes. The effectiveness of the proposed imIFML technique is also compared with the coIFML technique.
Highlights
In recent years, there has been rapid and significant development of renewable energy systems, including photovoltaic (PV) solar and wind power
This paper introduces an improved interval fuzzy modeling technique that is based on first-order models, modified linear programming, and actual boundary data points
Since the power obtained from the PV arrays PPV is often a non-negative value, in this case, Since the power obtained from the PV arrays is often a non-negative value, in this case, the lower bound f of the proposed improved interval fuzzy modeling (imIFML) is applied with the additional limit given by the the lower bound f imimIFML
Summary
There has been rapid and significant development of renewable energy systems, including photovoltaic (PV) solar and wind power. The high uncertainty of weather conditions makes forecasts of power profiles from renewable energy resources important for energy management, especially in large solar PV and wind power plants [1,2]. The very short-term forecast is for a period of a few seconds to a few minutes. The short-term forecast is for a period of several minutes to three days. The medium-term forecast is for a period of several days to one week. The long-term forecast is for a period of one month to several years. The medium-term and long-term energy forecasts are usually computed on a large centralized server of energy producers or utility companies with big data. Depending on renewable power profiles and desired purposes in grid operations, the very
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