Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure–correlate–predict

Abstract

Highlights Wind resource predicted at 37 sites using operational forecast data and MCP  Forecast reference data highly competitive with nearby meteorological observations  Systematic improvement when using forecast data at coastal sites AbstractOutput from a state-of-the-art, 4 km resolution, operational forecast model (UK4) was investigated as a source of long-term historical reference data for wind resource assessment. The data were used to implement measure-correlate-predict (MCP) approaches at 37 sites throughout the United Kingdom (UK). The monthly and hourly linear correlation between the UK4-predicted and observed wind speeds indicates that UK4 is capable of representing the wind climate better than the nearby meteorological stations considered. Linear MCP algorithms were implemented at the same sites using reference data from UK4 and nearby meteorological stations to predict the long-term (10-year) wind resource. To obtain robust error statistics, MCP algorithms were applied using onsite measurement periods of 1-12 months initiated at 120 different starting months throughout an 11 year data record. Using linear regression MCP over 12 months, the average percentage errors in the long-term predicted mean wind speed and power density were 3.0% and 7.6% respectively, using UK4, and 2.8% and 7.9% respectively, using nearby meteorological stations. The results indicate that UK4 is highly competitive with nearby meteorological observations as an MCP reference data source. UK4 was also shown to systematically improve MCP predictions at coastal sites due to better representation of local diurnal effects.2

Wind energy production uncertainty associated with wind assessments of various intervals

“…For example, the authors report that 19% of the total uncertainty is related to measurement uncertainty, while 13% is due to vertical extrapolation of wind speed data and 18% is due to the correlation of the data with the historical record. Meanwhile, Weekes et al (2015) performed wind resource assessments using an operational forecast model with 4 km resolution as the source of long-term historical data. Their results show that the operational model is capable of predicting the observed wind speeds better than a nearby met tower.…”

Wind Energy Potential Assessment Based on Wind Direction Modelling and Machine Learning

“…Of course, the numerical methods for determining of wind energy potential are also present in currently available literature [4,15,43]. Closely related with the wind potential assessment is the forecasting of the wind power plant power output in short-term [32] mid-term [14] and long-term [48] time series.…”

Influence of the ENSO and Monsoonal Season on Long-Term Wind Energy Potential in Malaysia

“…However, this literature utilizes short term data, that is, around one to three years of data from selected sites. As described in [5,6], wind potential analysis should consider more than 20 years of data to reflect the average conditions present over the lifetime of a wind project. The short term wind data are insufficient to characterize the inter-annual and monsoonal variability of the wind speed [7].…”