Abstract
Due to the low dispatchability of wind power, the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior 1-h periods (periods prior to the forecasting hour) chosen for the input layer parameters; and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecasting horizon, the greater the degree of improvement.
Highlights
A major impediment to the large-scale integration of wind power in electrical systems is the low dispatch‐ ability of this energy source
This paper focuses on models which employ the technique of arti‐ ficial neural networks (ANNs) to forecast wind power pro‐ duction [21], [22], [24]-[26], [28], [29]
For the various figures corresponding to the re‐ sults, t - 3 indicates that 2 periods prior to the forecasting pe‐ riod are chosen in addition to the forecasting period, and t + 3 indicates a forecasting horizon of 3 peri‐ ods starting from the period when the fore‐ casting is made
Summary
A major impediment to the large-scale integration of wind power in electrical systems is the low dispatch‐ ability of this energy source. The effects of variations in wind speed, and wind power, are observed on a year-to-year or season-to-season scale, and on a within-day scale [1]-[5]. A strategy that can be employed to Manuscript received: November 16, 2018; accepted: December 13, 2019. Date of online publication: April 29, 2020. This research was co-funded with ERDF funds, the INTERREG MAC 20142020 programme, within the ENERMAC project (No MAC/1.1a/117). No fund‐ ing sources had any influence on study design, collection, analysis, or interpreta‐ tion of data, manuscript preparation, or the decision to submit for publication
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