A Hybrid Method for Generation of Typical Meteorological Years for Different Climates of China

Since a representative dataset of the climatological features of a location is important for calculations relating to many fields, such as solar energy system, agriculture, meteorology and architecture, there is a need to investigate the methodology for generating a typical meteorological year (TMY). In this paper, a hybrid method with mixed treatment of selected results from the Danish method, the Festa-Ratto method, and the modified typical meteorological year method is proposed to determine typical meteorological years for 35 locations in six different climatic zones of China (Tropical Zone, Subtropical Zone, Warm Temperate Zone, Mid Temperate Zone, Cold Temperate Zone and Tibetan Plateau Zone). Measured weather data (air dry-bulb temperature, air relative humidity, wind speed, pressure, sunshine duration and global solar radiation), which cover the period of 1994–2015, are obtained and applied in the process of forming TMY. The TMY data and typical solar radiation data are investigated and analyzed in this study. It is found that the results of the hybrid method have better performance in terms of the long-term average measured data during the year than the other investigated methods. Moreover, the Gaussian process regression (GPR) model is recommended to forecast the monthly mean solar radiation using the last 22 years (1994–2015) of measured data.


Introduction
It is known that China is the most populous country in the world, with a population of more than 1.3 billion and covering an area of over 9.6 million km 2 .The fact that China ranks as the second largest consumer of energy raises concern about energy conservation and environmental protection [1][2][3].Solar energy, as a kind of renewable energy, is more energy-efficient and eco-friendly than oil and coal [4][5][6].Solar energy has received much attention in China as it is considered to meet a portion of China's energy demand.Quite a few weather files have been developed over the years for acquiring representative meteorological data, which is used to predict the annual performance of solar energy systems and evaluate building energy simulation [7][8][9].These weather files, known as test reference year (TRY) [10,11], design reference year (DRY) [12], and typical meteorological year (TMY) [13][14][15], are a representative database for one year and consist of a concatenation of 12 individual months selected from different years over the measured data duration.
The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) has built up a simple selection procedure to gather the climatic information in a TRY [16].In the process of a TRY selection, only one meteorological variable-dry-bulb temperature-is considered.More crucially, the available years, which contain months with extremely high or extremely low dry-bulb temperature, are ruled out until only one year remains, which is chosen to be the representative month of the TRY.

Climatic Zones and Data Collection
China is a vast country with a varied climate [2,36].Among the different ways to classify the climatic types in China, the temperature-strip method is recommended in this paper.According to this method [37,38], it can be divided into six climatic types based on annual accumulated temperature, which is obtained from the summation of the daily mean temperatures over 10 • C within a year, namely Tropical Zone (TZ) (>8000 • C), Subtropical Zone (SZ) (4500 • C-8000 • C), Warm Temperate Zone (WTZ) (3400 • C-4500 • C), Mid Temperate Zone (MTZ) (1600 • C-3400 • C), Cold Temperate Zone (CTZ) (<1600 • C) and the special zone-Tibetan Plateau Zone (TPZ).Figure 1 shows a general layout of the six major climate areas.
To cover the six major climate types, a total of 35 meteorological stations are taken into account in this study.The weather data (including daily air dry-bulb temperature, relative humidity, wind speed, pressure, sunshine duration and global solar radiation) in these cities are available from China meteorological stations.For each station, measured weather data cover at least 10 years during a period from 1 January 1994 to 31 December 2015.The 35 stations cover longitudes from 75 • 59 E (Kashgar) to 130 • 17 E (Jiamusi), latitudes ranging from 18 • 14 N (Sanya) to 53 • 28 N (Mohe), and have considerably variable altitude from 2.5 m (Tianjin) to 4507 m (Nagqu).
Information on the selected 35 typical stations is given in Table 1.Information on the selected 35 typical stations is given in Table 1.For the data shown in Table 1, missing and invalid measurements account for 0.32% of the database.Using interpolation, the missing and invalid measurements are usually replaced with the values for previous or subsequent days.Moreover, if more than 5 days' measured data are not available in a month, the month will be eliminated from the database [30].

Description of Methodologies for TMY Generation
It is well known that the typical meteorological year can be obtained through a number of methods, like the Danish method, the Festa-Ratto method, the typical meteorological year method, etc.In this part, the three methods are introduced in their original form with some variations in selection procedures.In view of the actual situation in China and the characteristics of solar energy systems, different meteorological indices are applied in this paper.In addition, a hybrid method is proposed aiming for generating TMY for 35 stations in China.The TMY which is available from the hybrid method has minimal differences from long-term average measured data in every month and is selected from a mixture of the results from the three methods.

The Danish Method
The Danish method was initially proposed by Lund and Eidorff [39], and several researchers, such as Janjai and Deeyai [18], and Skeiker [19], have contributed to its improvement and promotion.According to this method, seven daily meteorological parameter indices are cited for selection of typical meteorological months (TMMs) for each of the selected 35 meteorological stations: maximum air dry-bulb temperature, mean air dry-bulb temperature, mean air relative humidity, mean wind speed, mean pressure, sunshine duration and global solar radiation.This is an approach that uses a 3-step procedure to select individual months from different years during the measuring period.
In the first step, by considering the characteristics of solar energy systems, only three daily meteorological parameter indices are taken into account, namely, maximum air dry-bulb temperature, mean air dry-bulb temperature, and global solar radiation.
To eliminate seasonal variation, daily meteorological parameter indices are converted into daily residuals with regard to the smoothed daily long-term values obtained by Fourier analysis: where Y(y, m, d) is the residual of meteorological parameter index x for year y, month m, and day d, with respect to the smoothed daily long-term mean µ x (m, d) as calculated over the available years.
For each individual month, absolute values for the standardized mean f µ (y, m) and the standardized standard deviation f σ (y, m) of the residuals obtained using Equation (1) are calculated as follows: where µ Y (y, m) is the monthly mean and σ Y (y,m) is the standard deviation of the Y(y,m,d) for the year y, month m; µ µY (y) and σ µY (y) are the mean and standard deviation of µ Y (y,m) for year y; µ σY (y), σ σY (y) are the mean and standard deviation of σ Y (y,m) for year y.Thus, each individual month is characterized by six values, while three meteorological parameter indices are used in all.Then, the six values of f µ (y,m) and f σ (y,m) for each individual month are compared to select the maximal value (f max (y,m)): Energies 2016, 9, 1094 where (y,m,j) represents the standardized mean or standardized standard deviation for meteorological parameter index j for year y, month m.For month m, the first three months will be selected as priority candidate months when the months during available years are ranked in ascending order according to the value for f max (y,m).
In the second step, the long-term and short-term mean values of the seven daily meteorological parameter indices are calculated.If the short-term mean value of parameter index x for year y, month m differs by more than one standard deviation from the long-term mean value of the respective month, the month scores 0. Otherwise, a score of 1 is given to the month.The final score of each individual month is the sum of the scores, with a maximum value of 7. In the last step, among the three priority candidate months, the month with the highest score is included in the TMY.

The Festa-Ratto Method
The Festa-Ratto method is a modification of the Danish method and involves a rather complicated statistical treatment of the data.For the purposes of this study, seven daily meteorological parameter indices which are similar with that in the Danish method are utilized for this method.
In step 1, the daily meteorological parameter indices are converted into standardized residuals with respect to the smoothed long-term values, obtained as follows: where X(y,m,d) is the standardized residual of meteorological parameter index x, for year y, month m, and day d, with respect to the smoothed long-term mean µ x (m,d) and standard deviation σ x (m,d) as calculated for the available years.
In step 2, the first-order product of the standardized residuals is calculated: The first-order products z(y,m,d) are converted into standardized residuals with respect to the smoothed long-term values using: where Z(y,m,d) is the standardized residual of new parameter index z for year y, month m, and day d, with respect to the smoothed long-term mean µ z (m,d) and standard deviation σ z (m,d) as calculated for the available years.Since the number of daily meteorological parameters involved is 7, there are 7 new parameter indices Z created in total.
In step 3, the short-term mean value µ X (y,m) and standard deviation σ X (y,m) for standardized residual X(y, m, d) for year y, month m are calculated.At the same time, the long-term mean value µ µX (m) and standard deviation σ µX (m) for month m during the available years are obtained based on µ X (y,m).A similar procedure is carried out to obtain µ Z (y,m), σ Z (y,m), µ µZ (m), and σ µZ (m).The short-term and long-term cumulative distribution function (CDF) for X(y,m,d) and Z(y,m,d) are also determined.
Based on the above results, the statistical distance between the short-term and the long-term mean values d av and standard deviations d sd , as well as the Kolmogorov-Smirnov parameter, d ks , are calculated for each X and Z parameter and each individual month as follows: Next, the composite distances d X (y,m) and d Z (y,m) for each daily meteorological parameter index are calculated using the following equations: where a = b ≈ 0.1.
In step 4, for each month, 14 sets of distances are obtained from Equations ( 14) and ( 15), and the maximum value is sorted to form a new set of distances for that month.Then the month with the minimum distance in the new set is selected to be a member of the TMY.This min-max approach is shown as follows:

The Modified Typical Meteorological Year (TMY) Method
The TMY method, primarily proposed by Sandia National Laboratories, is one of the most popular methods for determining typical years.In this method, a set of 12 typical meteorological months (TMMs) is selected from a multi-year database using the Finkelstein-Schafer (FS) statistical method [40].Unlike the two methods described above, this method primarily pays attention to eight daily meteorological parameter indices to select typical months: maximum air dry-bulb temperature, mean air dry-bulb temperature, minimum air dry-bulb temperature, mean air relative humidity, minimum air relative humidity, maximum wind speed, mean wind speed, and global solar radiation.The selection procedure for the TMY consists of two steps.
In the first step, for each month of the different years, five candidate months having a CDF closest to the respective long-term distributions are selected.This selection is based on the variation between annual CDF and long-term CDF for the month in question.Moreover, to measure the variation, an empirical CDF for each meteorological parameter is determined using the following function: where S n (x) is the value of the CDF for parameter index x; i is the rank order number.n is the total number of meteorological parameters.From its definition, S n (x) is a monotonic increasing function with steps of sizes 1/n occurring at x i and is bounded by 0 and 1.Then the value of FS statistics of each parameter is obtained using: where FS x (y,m) is the FS statistic for year y, month m; CDF m is the long-term and CDF y,m is the short-term CDF of parameter index x for month m; and N is the number of daily readings of the month.The weighted sum (WS) of the FS statistics is derived by applying weighting factors WF x to the FS statistics values corresponding to each specific month in the selected period: where WS(y,m) is the weighted sum of the FS statistics for eight meteorological parameter indices for year y, month m; WF x is the weighting factor for parameter index x; M is the number of meteorological parameter indices.Furthermore, the five months with lowest WS values are selected to be candidate months.
It is worth mentioning that the weighting factors are essential for choosing TMY from the measured data.In consideration of the fact that this criterion is mainly applied to solar energy systems, global solar radiation gets the highest value among weighting factors.The assigned weighting factors are shown in Table 2.In the second stage, among various methods [10,25] for selecting TMMs from the five candidate months, a simpler selection process [26,42], starting with calculation of the root mean square difference (RMSD), is adopted.The RMSD is defined as follows: where RMSD is the root mean square difference of global solar radiation; H y,m,k is the value of daily global solar radiation for year y, month m and day k; H ma is the long-term mean value of global solar radiation for the month m; and N is the number of daily readings of the month.The month with the minimum RMSD is finally selected as the TMM.

TMY Selection Procedure
The final TMY selection is based on the hybrid method, by which the results of the Danish method, the Festa-Ratto method, and the modified typical meteorological year method are combined.After obtaining TMYs using the aforementioned methods, those results having the minimum differences from long-term average measured data for each month will be used to form a typical meteorological year.The selection procedure is described as below: First, for the three TMYs determined using the above methods, the values of indices 1, 2, 3, 4, which correspondingly represent the daily average values of global solar radiation, air dry-bulb temperature, mean air relative humidity, and wind speed, are compared with daily mean long-term average measured data for the same parameter indices by applying RMSD.The definition of RMSD for global solar radiation is shown in Equation (21), and that for other indices likes it.
Next, the sum of yearly values of RMSD (SYRMSD) are respectively calculated for the four mentioned parameter indices for each method: where p is the number of the index; i represents the month number.
Finally, the highest ranked one among the results of three months for every month, in ascending order of the ERMSD, is used in the TMY.The ERMSD parameter is defined using this equation: where i is the number of the month; RMSD 1 i is the root mean square difference of index 1 for month i; SYRMSD 1 is mean yearly values of RMSE of index 1; RMSD 2 i and SYRMSD 2 are for index 2; RMSD 3 i and SYRMSD 3 are for index 3; and RMSD 4 i and SYRMSD 4 are for index 4.

Performance Comparison
Application of the selection procedures described above and the data at the 35 stations provided in Table 1 generates the TMYs for 35 stations.Table 3 provides the TMYs data obtained using the Danish method (TMY_D), the Festa-Ratto method (TMY_F), and the modified typical meteorological year method (TMY_M) for six stations.Table 3. TMYs obtained using the Danish method, Festa-Ratto method, and modified typical meteorological year method for 6 different cities in China.

Station Method Month
The selected cities (Haikou, Shanghai, Zhengzhou, Yinchuan, Mohe, and Lhasa) respectively represent the six different climate types (TZ, SZ, WTZ, MTZ, CTZ, and TPZ) and provide a good sample of the range of latitude, longitude, and elevation.In Table 3, it can be seen that for each city the TMY comprises 12 individual months selected from different years of the measuring period for each particular method.Taking Lhasa (TPZ) as an example, it is apparent that a year considered typical for a certain month might not be inevitably typical for another month.For instance, January 1994 is selected as a TMM with TMY_F, while February is the one in 2007 in the same TMY.What is more, the composition of TMYs generated using the three methods is not identical for selected cities.
To gain a good understanding of selection patterns, we consider Lhasa again as an example for pictorial display.The values for RMSD of the three methods are computed and separately shown for the four meteorological parameter indices of Lhasa in Figures 2-5.In Figure 2, most of the result for global solar radiation obtained from TMY_M is the smallest for each individual month of the year.At the same time, the air relative humidity result of TMY_M, which is plotted in Figure 4, has greater agreement with those obtained from the measuring period data than do the air relative humidity results from TMY_D and TMY_F for most months of the year.It can be also confirmed from Figures 3  and 5 that the minimum RMSD for dry-bulb temperature and wind speed are respectively produced by TMY_D and TMY_F for the majority of months.
The selected cities (Haikou, Shanghai, Zhengzhou, Yinchuan, Mohe, and Lhasa) respectively represent the six different climate types (TZ, SZ, WTZ, MTZ, CTZ, and TPZ) and provide a good sample of the range of latitude, longitude, and elevation.In Table 3, it can be seen that for each city the TMY comprises 12 individual months selected from different years of the measuring period for each particular method.Taking Lhasa (TPZ) as an example, it is apparent that a year considered typical for a certain month might not be inevitably typical for another month.For instance, January 1994 is selected as a TMM with TMY_F, while February is the one in 2007 in the same TMY.What is more, the composition of TMYs generated using the three methods is not identical for selected cities.
To gain a good understanding of selection patterns, we consider Lhasa again as an example for pictorial display.The values for RMSD of the three methods are computed and separately shown for the four meteorological parameter indices of Lhasa in Figures 2-5.In Figure 2, most of the result for global solar radiation obtained from TMY_M is the smallest for each individual month of the year.At the same time, the air relative humidity result of TMY_M, which is plotted in Figure 4, has greater agreement with those obtained from the measuring period data than do the air relative humidity results from TMY_D and TMY_F for most months of the year.It can be also confirmed from Figures 3 and 5 that the minimum RMSD for dry-bulb temperature and wind speed are respectively produced by TMY_D and TMY_F for the majority of months.The selected cities (Haikou, Shanghai, Zhengzhou, Yinchuan, Mohe, and Lhasa) respectively represent the six different climate types (TZ, SZ, WTZ, MTZ, CTZ, and TPZ) and provide a good sample of the range of latitude, longitude, and elevation.In Table 3, it can be seen that for each city the TMY comprises 12 individual months selected from different years of the measuring period for each particular method.Taking Lhasa (TPZ) as an example, it is apparent that a year considered typical for a certain month might not be inevitably typical for another month.For instance, January 1994 is selected as a TMM with TMY_F, while February is the one in 2007 in the same TMY.What is more, the composition of TMYs generated using the three methods is not identical for selected cities.
To gain a good understanding of selection patterns, we consider Lhasa again as an example for pictorial display.The values for RMSD of the three methods are computed and separately shown for the four meteorological parameter indices of Lhasa in Figures 2-5.In Figure 2, most of the result for global solar radiation obtained from TMY_M is the smallest for each individual month of the year.At the same time, the air relative humidity result of TMY_M, which is plotted in Figure 4, has greater agreement with those obtained from the measuring period data than do the air relative humidity results from TMY_D and TMY_F for most months of the year.It can be also confirmed from Figures 3 and 5 that the minimum RMSD for dry-bulb temperature and wind speed are respectively produced by TMY_D and TMY_F for the majority of months.Next follows the calculation of the sum of yearly values for RMSD of the four main indices.Table 4 provides the values for ERMSD, which are assigned to the respective months using Equation (22).The ERMSDs often differ from month to month in a typical meteorological year, as well as vary in approach to each month as shown in Table 4.Moreover, the months with the smallest ERMSD values are shown with bold characters.In the end, the selected method for each month is determined by the minimum value of ERMSD.The smaller the ERMSD is, the better agreement will be with the mean measured data over time.The information about ERMSD for each candidate month in Lhasa is tabulated in Table 4.As demonstrated, the numbers printed in bold cells identify the TMMs.The same procedure is applied to other 34 stations and the results are listed in Table 5.Moreover, the monthly mean solar radiation data and monthly mean wind speed acquired from TMYs data for 35 stations are given in Tables A1 and A2, respectively.Next follows the calculation of the sum of yearly values for RMSD of the four main indices.Table 4 provides the values for ERMSD, which are assigned to the respective months using Equation (22).The ERMSDs often differ from month to month in a typical meteorological year, as well as vary in approach to each month as shown in Table 4.Moreover, the months with the smallest ERMSD values are shown with bold characters.In the end, the selected method for each month is determined by the minimum value of ERMSD.The smaller the ERMSD is, the better agreement will be with the mean measured data over time.The information about ERMSD for each candidate month in Lhasa is tabulated in Table 4.As demonstrated, the numbers printed in bold cells identify the TMMs.The same procedure is applied to other 34 stations and the results are listed in Table 5.Moreover, the monthly mean solar radiation data and monthly mean wind speed acquired from TMYs data for 35 stations are given in Tables A1 and A2, respectively.Next follows the calculation of the sum of yearly values for RMSD of the four main indices.Table 4 provides the values for ERMSD, which are assigned to the respective months using Equation (22).The ERMSDs often differ from month to month in a typical meteorological year, as well as vary in approach to each month as shown in Table 4.Moreover, the months with the smallest ERMSD values are shown with bold characters.In the end, the selected method for each month is determined by the minimum value of ERMSD.The smaller the ERMSD is, the better agreement will be with the mean measured data over time.The information about ERMSD for each candidate month in Lhasa is tabulated in Table 4.As demonstrated, the numbers printed in bold cells identify the TMMs.The same procedure is applied to other 34 stations and the results are listed in Table 5.Moreover, the monthly mean solar radiation data and monthly mean wind speed acquired from TMYs data for 35 stations are given in Tables A1 and A2, respectively.Also, Table 6 shows the selected times of each year for 12 TMMs in total.It is clear that 2008 is the most frequent year while the least frequent year is 2012, which is selected eight times altogether.According to the summary, it can be concluded that 2008 follows long-term weather patterns more closely than the others over the period of 1994-2015.Moreover, for different months the times may vary for the same year, and 12 and 0 are the largest and lowest numbers, respectively.That is to say, a particular month is selected for no more than 12 cities among the selected stations.
It can be clearly seen that the solar radiation data obtained from the four methods all agree well with the measured data during the period 1994-2015.Moreover, the hybrid method performs better than other three methods especially for the four stations, Zhengzhou (WTZ), Yinchuan (MTZ), Mohe (CTZ) and Lhasa (TPZ).Additionally, the prediction of monthly mean solar radiation is researched in the paper.The excellence and distinctive features of Gaussian Process Regression (GPR) forecasting model include its output probability distribution characteristic and capabilities to adaptively obtain the hyper-parameters in the model [43,44].In this part, the GPR model is recommended to forecast the monthly mean solar radiation by year 2016 using the last 22 years historical data.The selection of input variables includes solar radiation, dry-bulb temperature, relative humidity and wind speed in the last four years.
In order to test the forecasting performance of the GPR model, a simulation is carried out to forecast the monthly solar radiation in 2015.The index analysis of interval forecasting results under the 90% confidence level is shown in Table 7.It can be concluded that most of actual monthly mean solar radiation is within the confidence interval, and the forecasting results can well track the change of solar radiation from the view of MAPE values.The smaller the MAPE, the better the forecasting accuracy, which illustrates that the predictive value is closer to actual result.The best forecasting results are obtained in Lhasa and the interval width is narrower with the increasing forecasting accuracy.Besides, the FICP reduces due to the narrower interval width of smaller FIAW.The monthly mean solar radiation forecasting results by 2016 in different climates of China are shown in Table A3.It can be seen from the table that the predicted results have high similarities with historical data which indicates stable solar radiation change rules in these areas.In conclusion, the GPR forecasting model can directly generate the monthly mean solar radiation interval forecasting result rather than deterministic point value which reflects the uncertain change of future solar radiation.Further, the interval forecasting results can give more guiding significance for actual application related to energy areas.It can be clearly seen that the solar radiation data obtained from the four methods all agree well with the measured data during the period 1994-2015.Moreover, the hybrid method performs better than other three methods especially for the four stations, Zhengzhou (WTZ), Yinchuan (MTZ), Mohe (CTZ) and Lhasa (TPZ).Additionally, the prediction of monthly mean solar radiation is researched in the paper.The excellence and distinctive features of Gaussian Process Regression (GPR)

Conclusions
The generation of the TMY data is essential and important for solar energy utilization.In this paper, the performance of four TMY generation methods: the Danish method, the Festa-Ratto method, the Modified Typical Meteorological Year Method and the hybrid method are compared.These methods are used to generate and investigate TMYs for 35 stations in six different climatic zones of China using at least 10 years measured weather data, including air dry-bulb temperature, relative humidity, wind speed, pressure, sunshine duration and global solar radiation.Taking Lhasa as an example, the process of the hybrid method are presented and analyzed in this study.The monthly mean solar radiation data and monthly mean wind speed acquired from TMYs data, using the hybrid method, are appeared in the tabulation.There is a good agreement between the typical solar radiation data and the long-term measured data for the hybrid method on a monthly basis.Moreover, the proposed GPR model has good performance for forecasting monthly mean solar radiation.It is believed that the TMY data will have good impact on the related scientific research.Future work will focus on the in-depth long-term prediction of the climatology for different areas in China.We hope to report these findings in the near future.

Figure 2 .
Figure 2. RMSD results of global solar radiation by the three methods in Lhasa.

Figure 3 .
Figure 3. RMSD results of air dry-bulb temperature by the three methods in Lhasa.

Figure 2 .
Figure 2. RMSD results of global solar radiation by the three methods in Lhasa.

Figure 2 .
Figure 2. RMSD results of global solar radiation by the three methods in Lhasa.

Figure 3 .
Figure 3. RMSD results of air dry-bulb temperature by the three methods in Lhasa.

Figure 3 .
Figure 3. RMSD results of air dry-bulb temperature by the three methods in Lhasa.

Figure 4 .
Figure 4. RMSD results of air relative humidity by the three methods in Lhasa.

Figure 5 .
Figure 5. RMSD results of wind speed by the three methods in Lhasa.
Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 4 .
Figure 4. RMSD results of air relative humidity by the three methods in Lhasa.

Figure 4 .
Figure 4. RMSD results of air relative humidity by the three methods in Lhasa.

Figure 5 .
Figure 5. RMSD results of wind speed by the three methods in Lhasa.
Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 5 .
Figure 5. RMSD results of wind speed by the three methods in Lhasa.

Table 1 .
The main information of the 35 cities selected for the present study.

Table 1 .
The main information of the 35 cities selected for the present study.Number Location Latitude (N) Longitude (E) Elevation (m)

Table 2 .
Weighting factors for TMY type.

Table 4 .
ERMSD of the three candidate years for each month in Lhasa (the bold number shows the lowest ERMSD value in the month).

Table 5 .
The TMYs for the hybrid method of 35 cities in six different climatic zones of China.

Table 6 .
The year selection frequency of each month to be a TMM in the period of 1994-2015.

Table 7 .
The index results of monthly mean solar radiation forecasting for 2015 in different climates of China.

Table A3 .
The monthly mean solar radiation interval forecasting by 2016 in different climates of China.