QUANTIFYING THE ACCURACY OF THE USE OF MEASURE-CORRELATE-PREDICT METHODOLOGY FOR LONG-TERM SOLAR RESOURCE ESTIMATES

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1 QUANTIFYING THE ACCURACY OF THE USE OF MEASURE-CORRELATE-PREDICT METHODOLOGY FOR LONG-TERM SOLAR RESOURCE ESTIMATES Christopher Thuman Marie Schnitzer Peter Johnson AWS Truepower, LLC 463 New Karner Road Albany, NY ABSTRACT Several analytical methods have been applied to merge data sets with different periods of record. The Measure- Correlate-Predict (MCP) method is another potential solar resource assessment technique. This study examines the quality and consistency of MCP-based long-term solar resource estimates. The results of a case study indicate that MCP effectively minimizes the uncertainty in long-term solar resource assessment. 1. INTRODUCTION Obtaining direct solar resource measurements using on-site monitoring equipment can provide significant reductions in the uncertainty of a project s solar resource estimate. However, the short data samples obtained during solar resource assessment campaigns are generally insufficient to estimate the long-term solar resource with high confidence. Therefore, incorporation of consistent, longer-term reference data sets often increases confidence in the resulting long-term solar resource estimates. Several analytical methods have been applied to merge data sets with different periods of record. The Measure- Correlate-Predict (MCP) method is another solar resource assessment technique whereby a statistical relationship between concurrent data records is used to adjust short term data samples to the expected long-term conditions. While variations of this method have been widely examined and applied within the wind industry, its use for solar resource assessment has not been thoroughly examined. This study examines the quality and consistency of MCPbased long-term solar resource estimates developed using long-term data sets obtained from several NREL-sponsored surface stations. To simulate typical solar resource assessment campaigns, the NREL data sets were divided into multiple independent integer-year samples that were correlated with reliable satellite-modeled long-term datasets spanning 14 years. The respective long-term estimates were then compared with each other and the observed solar resource for the full period of record to determine their accuracy and precision. Comparisons of the results relative to other industry-accepted methods are provided. 2. BACKGROUND To accurately quantify the long-term wind or solar energy potential of a project site where on-site measurements are available, several analytical methods have been applied to merge data sets with different periods of record. In the wind industry, several methods have evolved and can be applied to solar resource assessment. The first, known as the ratio method, is suggested by the National Renewable Energy Laboratory (NREL) 1 and described by Gueymard and Wilcox 2. This method involves calculation of hourly or monthly ratios during a period of concurrent data between two data sets, and applying these ratios to the long-term hourly or monthly means, thereby producing a long-term estimate for the site. A second method involves combining two data sets by providing a weight to each, either equally or based on the uncertainty of each data set. It has been shown that the use of two or more data sets can have an improved quality on the combined data set 3.

2 The Measure-Correlate-Predict (MCP) method is a solar resource assessment technique where a statistical relationship between concurrent data records is used to adjust short term data samples to the expected long-term conditions. While variations of this method have been widely examined, accepted, and applied within the wind industry, its use for solar resource assessment has not been thoroughly examined. 2.1 Measure-Correlate-Predict Methodology Since the solar and meteorological resource can vary over timescales of months to years, it is important to adjust data collected at a site having a short period of record to represent historical conditions as closely as possible. The proposed method to develop these site-specific solar resource characteristics is known as Measure-Correlate- Predict, or MCP. In MCP, data from a site is measured, ideally having high measurement accuracy. Upon completion of a year or more of measurements, a linear regression or other relationship is established between a target site, spanning a relatively short period, and a reference site, spanning a much longer period. The complete record of the reference station is then used in this relationship to predict the long-term historical climate at the target site. The purpose of this is to combine data having a short period of record but site-specific seasonal and diurnal characteristics with a data set having a long period of record but not necessarily site-specific characteristics. Assuming a strong correlation, the strengths of both data sets are captured and the uncertainty in the long-term estimate can be reduced. The most important factors determining the success of MCP are the choice of reference station(s), particularly the quality of its relationship with the target site, which should ideally be linear with a high correlation coefficient, and the consistency and length of the reference data record. Additionally, when less than a full year of data is available from the target site, the possibility of a seasonal bias in the relationship between the target site and reference site must be considered. 2.2 Data Sources In order to obtain the best results for this study, solar radiation data from stations having a long period of record, data of the highest quality, and a consistent long-term annual trend were selected. A geographically diverse sample was selected to demonstrate the practicality of Measure- Correlate-Predict in multiple environments. To meet these criteria, data from three NREL-sponsored stations at Bondville, Illinois, Desert Rock, Nevada and Penn State, Pennsylvania were selected. Basic information about these stations is shown in Table 1. TABLE 1. INFORMATION ABOUT NREL STATIONS USED Site Lat Lon Elev POR Used Sensor Bondville Desert Rock Penn State These stations were used to represent site-specific measurements, simulating a monitoring campaign that would be undertaken by a solar project developer. A common satellite-based model was selected as the long-term reference for this analysis. Satellite-modeled data is frequently used in solar resource assessment due to its long period of record, consistency over time, availability over large spatial areas and ability to model the solar resource specific to a project site. Ideally, a surface-based data set is used as a reference data source, but in most cases highquality long-term measured data within the immediate vicinity of a project site is rare. Fourteen years of satellite modeled GHI data representing each of the three NREL sites was used for this analysis. 3. METHODOLOGY Considering the long period of record at each NREL station, the data was broken into several sub-periods to simulate several different periods of on-site data collection. Subperiods of 1-month, 3-months, 6-months, 12-months, 18- months, two years, three years, four years, five years and six years were analyzed to assess the variability of long-term estimates. Period of record was chosen based on the following criteria: Monthly periods correspond to calendar months The first 3-month period was selected starting with the first month of concurrent data with subsequent periods starting exactly 3 months after the previous period The first 6-month period was selected staring with the first month of concurrent data with subsequent periods lagging the previous period by 3 months Data periods of 12-months or greater were selected starting with the first month of data with subsequent periods lagging the previous by 6 months There are overlapping data in the tested periods of 6 months or greater. Applying the assumption that each selected data

3 period corresponds to an independent monitoring duration, data overlap is not considered to be a factor in this analysis. For each sub-period, a daily linear regression was established between the NREL-measured GHI data and concurrent satellite-modeled GHI data. The 14-year mean GHI estimated by the satellite model was then applied to each regression equation, resulting in a long-term GHI estimate specific to each site. This result was then compared to the 11 or 12-year mean observed GHI at each NREL station, which was assumed to be the true long-term irradiation for the purposes of this analysis. Sub-periods where the number of missing records between the two data sources was greater than one were removed. For each sub-period, the long-term mean global solar irradiation estimates were averaged and the corresponding standard deviation of all results was calculated as an approximate estimate of the uncertainty associated with the MCP process RESULTS AND DISCUSSION Average Absolute Bias of Monitoring Periods The difference between the estimated long-term GHI and actual long-term GHI measured by NREL for each assumed monitoring period was evaluated at each site. Figure 1 shows the absolute difference between the estimated and actual long-term GHI for each monitoring period at each site. Absolute Difference from Long-Term 6% 5% 4% 3% 2% 1% Desert Rock Bondville Penn State 0% 1-mo 3-mo 6-mo 1-yr 1.5-yr 2-yr 3-yr 4-yr 5-yr 6-yr Period of Record Fig 1. Absolute Difference between Estimated and Actual Long-Term GHI for each Monitoring Period The results show greater than a 3.5% difference at Desert Rock and greater than 4.5% for Bondville and Penn State when relying on 1-month of on-site measurements. The accuracy of the estimates increases remarkably as monitoring period length increases through one year. This shows the importance of on-site measurements in accurately characterizing the long-term mean GHI. Based on the results discovered through examination of various modeled reference data sources to accurately quantify a site s mean annual solar resource 4, having 1- month of on-site measurements for use in a MCP assessment results in the same level of accuracy in the longterm estimate as relying on various modeled data sources. Although the uncertainty attributed to measurement may be less, the accuracy of the long-term estimate is subject to error. Figure 1 shows that collecting a year or more of onsite measurements provides the most drastic reduction in the difference between estimated and true long-term GHI. There was no clear relative bias in the results, which provides another indicator of the accuracy of the linear regression MCP methodology. In some cases, the predicted long-term solar resource was consistently greater than the true long-term GHI, while in other cases it was consistently lower., 4.2 Standard Deviation of Bias at Monitoring Periods The results from this work show how on-site monitoring period length minimizes uncertainty in the long-term GHI estimate. Figure 2 contains a plot of the mean relative standard deviation of the linear regressions for each site as a function of monitoring period. Relative Standard Deviation 8% 7% 6% 5% 4% 3% 2% 1% Desert Rock Bondville Penn State 0% 1-mo 3-mo 6-mo 1-yr 1.5-yr 2-yr 3-yr 4-yr 5-yr 6-yr Period of Record Fig 2. Mean Relative Standard Deviations for each Monitoring Period The 1-month monitoring period resulted in an uncertainty range between 5.0% and 7.5% for the long-term solar radiation estimates, while monitoring for six months decreases the uncertainty from 1.5% to 4.5%. Although uncertainty was lower for the 3- and 6-month monitoring periods, the overall magnitude was still significantly larger than the uncertainty for monitoring periods of a year or longer. High uncertainty associated with results for less than 12 months of on-site data indicates the significant seasonal influence on the quality of long-term GHI estimates.

4 Once the period of record reaches 12 months, there are large drops in the associated uncertainties. After 24-months of onsite data collection, there are only marginal improvements in accuracy across all sites examined. These results provide evidence that having 12 to 24 months of on-site measurements can significantly reduce uncertainty in longterm GHI estimates, implying that on-site measurement campaign lengths can be targeted to be within this range. 4.3 Desert Rock Case Study Since the results were similar for all sites examined, the use of this method for long-term solar resource assessment can be applied to all climate types. However, the data suggested the results were better at Desert Rock. While data quality may be a factor, the low cloud frequency and less seasonal variability of the desert climate are likely contributing factors to these results. Additionally, satellite models likely perform more accurately in this environment, thus contributing to a higher-accuracy long-term assessment. Considering this and the general preference and greater interest of PV development in climates similar to Desert Rock, the accuracy of the MCP methodology at this location was further investigated. Table 2 shows the percentage of long-term projections that arrived within a certain range of the true long-term mean for each monitoring period examined. TABLE 2. PERCENT OF LONG-TERM ESTIMATE COMPARED TO LONG-TERM MEAN Monitoring Period % of Projections within Various Percentages of Long-Term Mean <1% <2% <3% <5% 1-month month month month month year year year year year Having a longer monitoring period increased the likelihood that the predicted mean would be closer to the long-term mean. After 36 months of monitoring at Desert Rock, the long-term estimated mean was within 2.0% of the true mean in every case, and the results were within 3.0% every time after 18 months of monitoring. Only when the monitoring period lasted three months or less was the predicted value greater than 5.0% of the true value. 4.4 Comparison with Ratio Method To compare the accuracy of these results with those from a different method of long-term adjustment, a similar solar resource assessment was performed at Desert Rock using the ratio method proposed by NREL. The same concurrent periods of on-site and satellite-modeled reference data were used. One-year periods having high data recovery were selected, and a monthly ratio was developed between the on-site data and reference data. The monthly ratios were then applied to the long-term monthly GHI estimated by the reference data set, resulting in a long-term monthly GHI estimate for the site, which could then be compared to the actual long-term monthly and annual values. Based on the 15 1-year periods examined, the standard deviation of the relative difference between estimated long-term annual GHI and the true long-term GHI at Desert Rock was approximately 1.3%. Since the results indicated a 0.9% relative standard deviation using 1-year of observed data at Desert Rock and the linear regression MCP methodology, comparable results to the industry-accepted ratio method were obtained. The standard deviations of both approaches indicate that the particular year of selected on-site data was more likely to impact the results using the ratio method than when a linear regression approach was considered. 5. CONCLUSION This analysis examined the appropriateness of the linear regression MCP methodology to estimate the long-term GHI for solar resource assessments. The results show that this method, which is widely used in the wind industry, also reduces uncertainty for long-term GHI estimates in the solar industry. Based on results from three locations in the United States with high-quality and long-period-of-record solar radiation measurements, linear-regression MCP has been shown to provide low-uncertainty long-term estimates for the GHI component in multiple climates, with the most accuracy in desert environments. A case study at Desert Rock in southern Nevada shows that MCP using linear regression performed better in predicting the long-term solar resource than another industry-recognized method. 6. REFERENCES (1) Stoffel, T. et al; Concentrating Solar Power: Best Practices Handbook for the Collection and Use of Solar Resource Data. National Renewable Energy Laboratory Technical Report NREL/TP September 2010.

5 (2) Gueymard, C. A. and S. M. Wilcox (2009). Spatial and Temporal Variability in the Solar Resource: Assessing the Value of Short-Term Measurements at Potential Soalr Power Plant Sites. Boulder, CO: ASES. Solar 2009 Conference, Buffalo, NY, May (3) Meyer, R. et al. (2008). Combining Solar Irradiance Measurements and Various Satellite-Derived Products to a Site-Specific Best Estimate. Solar PACES Symposium, Las Vegas, NV, (4) Schnitzer, M. et al. (2012). Understanding the Variation in Estimated Long-Term Solar Resource Estimates: Which Data Set Accurately Represents Your Project Site? World Renewable Energy Forum, Denver, CO, 2012.