Estimation of the Monthly Average Daily Solar Radiation using

Article pubs.acs.org/est

Estimation of the Monthly Average Daily Solar Radiation using Geographic Information System and Advanced Case-Based Reasoning Choongwan Koo, Taehoon Hong,* Minhyun Lee, and Hyo Seon Park Department of Architectural Engineering, Yonsei University, Seoul, 120-749, Korea S Supporting Information *

ABSTRACT: The photovoltaic (PV) system is considered an unlimited source of clean energy, whose amount of electricity generation changes according to the monthly average daily solar radiation (MADSR). It is revealed that the MADSR distribution in South Korea has very diverse patterns due to the country’s climatic and geographical characteristics. This study aimed to develop a MADSR estimation model for the location without the measured MADSR data, using an advanced case based reasoning (CBR) model, which is a hybrid methodology combining CBR with artificial neural network, multiregression analysis, and genetic algorithm. The average prediction accuracy of the advanced CBR model was very high at 95.69%, and the standard deviation of the prediction accuracy was 3.67%, showing a significant improvement in prediction accuracy and consistency. A case study was conducted to verify the proposed model. The proposed model could be useful for owner or construction manager in charge of determining whether or not to introduce the PV system and where to install it. Also, it would benefit contractors in a competitive bidding process to accurately estimate the electricity generation of the PV system in advance and to conduct an economic and environmental feasibility study from the life cycle perspective.

1. INTRODUCTION In keeping with the widespread awareness of the issues on global warming, the United Nations Framework Convention on Climate Change (UNFCCC) was signed in June 1992 based on the principle of limiting the use of fossil fuels. To promote this convention, the Conference of Parties (COP) is annually held. During the third COP in December 1997, the Kyoto Protocol, a concrete plan of the UNFCCC, was adopted. In order to acquire flexibility in the responsibility to enforce greenhouse gas reduction, together with the protocol, various systems, including emission trading scheme, joint implementation, and clean development mechanism, were introduced. Due to the fortification of international environmental regulations, the South Korean government set a goal to reduce business-asusual carbon emissions in 2020 by 30% to increase the ratio by 50% in 2050. The South Korean National Assembly recently passed “The Act on Allocation and Trading of Greenhouse Gas Emissions,” and from 2015, the country plans to enforce the greenhouse gas emission trading scheme.1,2 As part of the global effort to reduce greenhouse gas emissions, there has been a growing interest in new renewable energy sources. Particularly, advanced countries like Germany, Japan, U.S., and the UK have introduced Feed in Tariff (FIT), renewable portfolio standard (RPS), etc., resulting in promoting the growth of the new renewable energy market.3−8 There is a growing interest in various types of new renewable energy sources, such as solar © 2013 American Chemical Society

energy, wind energy, bioenergy resources, and hydroelectricity.9−13 Through the revisions of laws in February 2012, the South Korean government introduced RPS, which substituted for the existing FIT, and also granted renewable energy certificates (RECs) to business sectors participating in RPS. Particularly, it proposed the mandatory amount of the annual solar photovoltaic (PV) system supply for the concentrated growth of the PV industry. Standards have been set to grant 1.5 of weighted values to PV businesses that use existing facilities.14 The trends in new renewable energy systems in South Korea showed that hydrogen fuel cells, the PV system, and the geothermal energy system increased annually by 162.2%, 131.1%, and 77.1%, respectively, which revealed that the PV system was the second most active system introduced in the country. Particularly, those in educational facilities showed that the PV system, the solar heat system, and the geothermal energy system increased annually by 864%, 492%, 691%, respectively, which revealed that the PV system was most actively introduced to these facilities.15 The floor area ratio of the educational facilities is less than that of multifamily housing units or offices, so it is expected that the introduction of the PV system will be more effective in such institutions. Received: Revised: Accepted: Published: 4829

September 18, 2012 February 7, 2013 April 2, 2013 April 2, 2013 dx.doi.org/10.1021/es303774a | Environ. Sci. Technol. 2013, 47, 4829−4839

Environmental Science & Technology

Article

Figure 1. Research framework.

were 1.637 MJm−2 and 0.813 MJm−2, respectively, showing it to be the best model.56 Third, other studies estimated the MADSR by using geographic information system (GIS). In Portugal, a study used LiDAR data based on the solar analyst extension for ArcGIS. If it is assumed to apply a 7-MW PV system to 538 buildings in Lisbon, about 11.5 GWh/year of potential value would be generated, offering 48% of the current electricity demand.57 In Canada, to estimate the MADSR on the rooftop in southeastern Ontario, GIS and advanced feature extraction algorithms were used. The potential peak power outputs and potential annual energy generation were 5.74 GW (157% of the region’s peak power demands) and 6,909 GWh (5% of Ontario’s total annual demand), respectively.58 In Saudi Arabia, to estimate the MADSR where it is not currently measured, a variogram model that used geostatistical techniques was developed, and it showed that the mean percentage errors were between 0.5% and 1.7% by month.59 As such, there have been many studies on the MADSR based on various aspects. Although the ANN methodology, which is often used in the previous studies, offers excellent prediction accuracy, its “black-box” makes it difficult to explain prediction results. The GIS-based methodology can make it easy to understand the MADSR distribution by visually expressing it on a map. However, it presents prediction results based on simple distances. Therefore, in developing a MADSR estimation model, this study proposes an advanced CBR methodology that not only combines the advantages of GIS’s visual expression and ANN’s excellent prediction accuracy, but also improves its explanatory power on prediction results. As shown in Figure 1, this study was conducted as follows: (i) it collected the MADSR data and related information (i.e., geographical and meteorological factors) measured in 15 regions in South Korea; (ii) using GIS, the MADSR data were expressed on a map along with the geographic information, and the seasonal nature of the MADSR distribution was analyzed; (iii) through the time-series analysis, it analyzed the monthly cycle of the MADSR and acquired the

While the PV system is an unlimited source of clean energy, allows for unmanned operation, and is in a modular structure that makes it possible to vary the size of the system, its electricity generation is dependent upon weather conditions, particularly the monthly average daily solar radiation (MADSR).16−28 Therefore, it is very important to determine accurate MADSR to maximize the effect of the introduction of the PV system. South Korea observes the MADSR in 24 regions, establishing a MADSR database for about 30 years. However, other than these 24 regions, the country has no actual MADSR data in other regions in which the PV system can be effectively introduced. To solve this issue, various studies have been conducted worldwide. Some representative studies are as follows.29−59 First, some studies aimed to estimate the MADSR by using artificial neural networks (ANN). In Iran, an ANN model was developed in estimating the MADSR. An ANN model used wind speed, the number of the day of the year (starting from the first of January), daily mean air temperature, relative humidity, and sunshine hours as independent variables. The mean absolute percentage error (MAPE) of this model was 5.21%, which was superior to the conventional method (10.02%).51 In estimating the MADSR in Nigeria, an ANN model was developed with latitude, longitude, altitude, month, mean temperature, mean sunlight duration, and relative humidity as independent variables.52 In estimating the MADSR in Indonesia, an ANN model was developed by visualizing the MADSR with a solar map. The MAPE of this model was excellent at 3.4%.53 Second, other studies estimated the MADSR by using methods other than ANN. A study estimated the MADSR in six regions in India by using the IrSOLaV/CIEMAT satellite model.54 A satellite-based model was used in estimating the MADSR in Cambodia, of which the root-mean-square deviation was 6.3%.55 In China, a support vector machines was used to estimate the MADSR. Applying Tmax (maximum air temperature)-Tmin (minimum air temperature) and Tmean (mean air temperature) as independent variables, this model’s root mean square error (RMSE) and nash-sutcliffe coefficient 4830

dx.doi.org/10.1021/es303774a | Environ. Sci. Technol. 2013, 47, 4829−4839


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phase was conducted to acquire the statistical feasibility. SI Table S2 shows the result of the time-series analysis (especially, the autocorrelation analysis) on each of the 15 regions. Within 5% of the significance level, if the absolute value of t-statistics to the autocorrelation coefficient with time lag 12 is larger than the warning level “1.25”, it can be said that seasonality in the time-series exist.65 As shown in SI Table S2, t-statistics to the autocorrelation coefficient in all 15 regions is larger than the warning level 1.25. Therefore, it can be said that seasonality, or the monthly cyclic nature, exists in time-series with time lag 12. 2.3. An Advanced CBR Model. It was determined that the MADSR data in South Korea have seasonality and a monthly cyclic nature. Based on such results, this study conducted cluster formation by dividing 1800 MADSR data into 12 months. The data included in each cluster, monthly data measured for the past 10 years (January 2001 to December 2010), consists of 150 sets (15 × 10). For each cluster, this study proposed a MADSR estimation model using an advanced CBR model, which is a hybrid methodology that is based on the fundamental characteristics of CBR (a methodology that retrieves similar cases among historical case-based data), and improves prediction accuracy by combining other methodologies like ANN, MRA, and GA. Refer to the previous studies for a more detailed explanation.66−69 2.3.1. Selection of Similar Projects Using CBR. CBR methodology retrieves prediction results based on case similarity (CS) and uses a series of processes to calculate attribute similarity (AS), attribute weight (AW), and CS, which can be expressed in a matrix, as in eq 1.

feasibility of the monthly data-based cluster formation; (iv) an advanced case-based reasoning (CBR) for estimating the MADSR was proposed. This model is a hybrid methodology that combines ANN, multi-regression analysis (MRA), and genetic algorithm (GA); (v) the feasibility of the MADSR estimated by the proposed model was verified through a case study. Electricity generation was predicted through a software program called RETScreen; and, finally; and (vi) it conducted an economic and environmental feasibility analysis based on the life cycle.

2. MATERIALS AND METHODS 2.1. Establishment of Database. Through extensive literature review and interviews with experts (i.e., MADSR experts in the Korea Meteorological Administration (KMA) and in the Korea Institute of Energy Research (KIER)), this study collected the MADSR data measured in a monthly cycle for the past 10 years (January 2001 to December 2010) in 15 regions in South Korea, and extracted factors affecting the MADSR60−64 (refer to Supporting Information (SI) Table S1). As shown in Table 1, this study selected as independent Table 1. Target Variable and Independent Variables Affecting the Monthly Average Daily Solar Radiation variables independent variable

target variable

attributes

detailed description

geographical factor

longitude latitude altitude

() °E () °N () m

meteorological factor

monthly mean percentage of sunshine monthly mean cloud amount monthly total of sunshine duration monthly mean temperature monthly mean relative humidity monthly mean wind speed

() %

monthly average daily solar radiation

() kWh/ m2/day

⎛ AS11 ··· AS1n ⎞⎛ AW1 ⎞ ⎛ CS1 ⎞ ⎜ ⎟⎜ ⎟ ⎟ ⎜ ⎜ ⋮ ⋱ ⋮ ⎟⎜ ⋮ ⎟ = ⎜ ⋮ ⎟ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎝ ASm1 ··· ASmn ⎠⎝ AWn ⎠ ⎝CSn ⎠

1−10 () day

(1)

where, AS is the attribute similarity, AW is the attribute weight, CS is the case similarity, m is the number of cases, and n is the number of attributes. Before applying CBR methodology, as in eq 1, the values of all independent variables need to be standardized, as in eq 2.

() °C () % () m/s

SV =

AV − AV min AV max − AV min

(2)

where, SV is the standardized value for the AV(actual value of attributes), AVmin is the minimum value of the AV, and AVmax is the maximum value of the AV. Using the standardized data by eq 2, AS and CS are calculated by eqs 3 and 4.

variables longitude, latitude, and altitude for geographical factors, while monthly mean percentage of sunshine, monthly mean cloud amount, monthly total of sunshine duration, monthly mean temperature, monthly mean relative humidity, and monthly mean wind speed were independent variables for meteorological factors. The MADSR was used as the target variable. 2.2. Cluster Formation. The MADSR distribution in South Korea was shown to have seasonal characteristics (refer to Figure 2 and SI Figures S1−S3). Based on this result, the MADSR data in South Korea is believed to have a monthly cyclic nature. To acquire the statistical feasibility of the monthly cyclic nature, this study conducted a time-series analysis. Generally, the time-series analysis consists of four phases: model identification, parameter estimation, diagnostic checking, and forecasting. This study aims to determine whether the MADSR data has seasonality based on the time-series analysis. Thus, the autocorrelation analysis in the model identification

⎧ ⎛ |SVTC − SVRC| ⎞ ⎪ × 100⎟ if fAS (x) ≥ MCAS ⎪100 − ⎜ SVTC ⎝ ⎠ fAS (x) = ⎨ ⎪ ⎪ 0 if fAS (x) < MCAS ⎩ (3)

where, fAS is the function for calculating the attribute similarity, SVTC is the standardized value for the AV of the test case, SVRC is the standardized value for the AV of the retrieved case, and MCAS is the minimum criterion for scoring the attribute similarity. n

fCS (x) =

∑i = 1 (fAW × fAS ) i

i

n

∑i = 1 (fAW ) i

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(4)



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Figure 2. Monthly average daily solar radiation by region for the spring season in South Korea.

where, f CS is the function for calculating the case similarity, fAW is the function for calculating the attribute weight, fAS is the function for calculating the attribute similarity, and n is the number of attributes. By multiplying the attribute weight with the attribute similarity, the weighted-attribute similarity was derived. Its accumulated sum was divided by the accumulated sum of the attribute weight. 2.3.2. Formation of Filtering Engine. Since CBR methodology presents both the prediction results and historical data, it has high explanatory power. However, according to previous studies, compared to MRA or ANN, its prediction accuracy is relatively low. Accordingly, to improve its prediction accuracy, a filtering engine is required. Through the following eqs 5−10, a filtering engine can be introduced, and the proposed hybrid model was defined as an advanced CBR model. fMAPE (x) =

100 × m

m

∑ i=1

AVi − PV AVi

fPA (x) = 100 − fMAPE (x)

where, f MAPE is the function for calculating the mean absolute percentage error, AV is the actual value of the target variable, PV is the predicted value of the target variable, m is the number of cases, and f PA is the function for calculating the prediction accuracy. ⎛ MAPEMRA ⎞ ⎟ PVMRA × ⎜1 − ⎝ ⎠ 100 ≤ PR MRA ⎛ MAPEMRA ⎞ ⎟ ≤ PVMRA × ⎜1 + ⎠ ⎝ 100

(7)

⎛ MAPEANN ⎞ ⎟ PVANN × ⎜1 − ⎝ 100 ⎠ ≤ PRANN

(5)

⎛ MAPEANN ⎞ ⎟ ≤ PVANN × ⎜1 + ⎝ 100 ⎠

(6) 4832

(8)



Article n

where, PRMRA is the predicted range of the MRA model, PVMRA is the predicted value of the MRA model, MAPEMRA is the mean absolute percentage error of the MRA model, PRANN is the predicted range of the ANN model, PVANN is the predicted value of the ANN model, and MAPEANN is the mean absolute percentage error of the ANN model.

MAE =

i=1 n

MAPE =

(9)

⎛ TRCRMA ⎟⎞ min(CRMA) × ⎜1 − ⎝ 100 ⎠ ≤ CRMS* (10)

where, CRMA is the cross-range between the predicted value of the MRA and ANN models, TRCRMA is the tolerance range of CRMA, and CRMA* is the filtering range in which TRCRMA was applied to CRMA. 2.3.3. Optimization with GA. To maximize the prediction performance on the process to develop an advanced CBR model, there are variables that need to be optimized. Such variables were defined as optimization parameters to which GA was applied to extract the optimal solution. In GA, a group of optimization parameters are defined as a chromosome. A chromosome consists of genes, each of which points to each optimization parameter.67 This study used the following four optimization parameters: MCAS, RAW, TRCRMA, and RCS. 2.4. Energy Simulation Using the Software Program Called RETScreen. Using the MADSR estimated by the advanced CBR model, simulation-based electricity generation was extracted by using the software program called RETScreen, which was codeveloped by specialists from the Department of Natural Resources in Canada and the United Nations Environment Programme.70,71 RETScreen software is widely used worldwide. As of 2010, more than 20 000 people have downloaded the RETScreen software, and thus it can be considered to be excellent software which can be used for evaluating renewable energy systems.72−77 It was validated that RETScreen software provided great prediction performance in error rate within 0−6% of the actual electricity generation.78,79 In this study, to verify the simulation-based electricity generation, the following assessment indices were used: (i) CV(RMSE) proposed by ASHRAE to verify the feasibility of the simulation result. CV (RMSE) was calculated by eq 11, and if the result is within 25%, the energy simulation was determined to be feasible;80 (ii) the three key assessment indices to assess the prediction accuracy between the actual and simulation-based electricity generationthat is, RMSE, MAE, and MAPE, as expressed in eqs 12−14. CV(RMSE) =

n ∑i = 1 (AEGi − SEGi)2 n 1 ∑i = 1 AEGi × n

×

1 n

n

NPV =

n

∑ (AEGi − SEGi)2 × i=1

1 n

∑ t=0

× 100

AEGi − SEGi 1 × × 100 n AEGi

(13)

(14)

Bt − (1 + r )t

n

∑ t=0

Ct (1 + r )t

(15)

where, NPV is net present value; Bt is benefit in year t; Ct is cost in year t; r is the real discount rate; and n is the period of the life cycle analysis Second, SIR, a relative evaluation method, means the ratio of benefit to cost generated in the life cycle. It is converted into the present worth by using the real discount rate. Generally, if

(11)

RMSE =

1 n

where, CV(RMSE) is the coefficient of the variation of the root-mean-square error, RMSE is the function for calculating the root-mean-square error, MAE is the function for calculating the mean absolute error, MAPE is the function for calculating the mean absolute percentage error, AEG is the actual electricity generation, SEG is the simulation-based electricity generation, and n is the number of data (months). 2.5. Economic and Environmental Assessment. For better understanding of the results, this study conducted an economic and environmental assessment.81−86 Particularly, from the environmental aspect, CO2 emission reduction due to electricity generation was converted into an economic value by using the profit from the sale of carbon credits, called “Korea Certified Emission Reductions (KCERs)” at $10.29/tCO2. It is necessary for the LCC and LCCO2 analyses to assume various factors, which can be divided into six factors: (i) the analysis approach; (ii) the real discount rate; (iii) the inflation and increase rates; (iv) the analysis period; (v) the starting point of the analysis; and (vi) the significant cost of ownership.87 SI Table S3 shows the assumption on key elements for the LCC and LCCO2 analyses. First, using the data from the Bank of Korea Economic Statistics system and the Korean Statistical Information Service, the real discount rate on the inflation rate (3.30%), the electricity price growth rate (0.66%), and the carbon dioxide emission trading price growth rate (2.66%) were calculated, respectively. Second, the analysis period should be established. Made of reinforced concrete structures (refer to SI Table S3), analysis period was set to 40 years according to the standard service life and service life scope chart for the reinforced concrete buildings under the Enforcement Regulations on Corporate Income Tax Act in Korea.1,2 Third, the significant cost of ownership for LCC analysis should be determined, which means to consider the initial construction cost, the operation and maintenance cost, and the demolition cost. This study assumed that waste disposal costs and salvage value offset each other. Therefore, only the initial construction cost and the operation and maintenance cost were considered. The details were determined from interviews with related experts. The LCC and LCCO2 analysis result can be expressed largely in two key indices: Net Present Value (NPV) and Saving−toInvestment Ratio (SIR). First, NPV, an absolute evaluation method, means the benefit generated in the life cycle enables savings in cost. It is converted into the present worth by using the real discount rate. Generally, if “NPV ≥ 0”, a given project is deemed feasible, and it is determined to have passed the break-even point (BEP). NPV was calculated using eq 15.

≤ CRMA

⎛ TRCRMA ⎞⎟ ≤ max(CRMA) × ⎜1 + ⎝ 100 ⎠

∑ i=1

max(min(PR MRA), min(PRANN)) ≤ min(max(PR MRA), max(PRANN))

∑ |AEGi − SEGi| ×

(12) 4833



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“SIR ≥ 1”, a given project is deemed feasible. SIR was calculated using eq 16. SIR =

n

S

n

I

indirect verification of the MADSR prediction results can be performed. In particular, by considering 4.31% of the prediction error, the MADSR in October 2011 is estimated to be between 3.35 and 3.65 kWh/m2/day. However, since there is no actual MADSR measured in the location of “S” elementary school, no direct verification can be performed. Thus, an alternative method of verification for the result of the case study is required. Electricity generation between January and December 2011 from the PV system that was introduced by “S” elementary school was selected as the alternative method for verification, and the verification process is as follows. • Step 1: Collect the geographical and meteorological factors in the location of “S” elementary school (refer to Table 1). • Step 2: Using the advanced CBR model proposed in this study, extract the prediction results on the MADSR in the location of “S” elementary school. • Step 3: Calculate the simulation-based electricity generation by applying the MADSR estimated by the advanced CBR model to the software program called RETScreen. • Step 4: Using the actual and simulation-based electricity generation at “S” elementary school, extract the coefficient of variation of the root mean square error (CV(RMSE)), RMSE, mean absolute error (MAE), and mean absolute percentage error (MAPE); • Step 5: For additional comparison, repeat Step 3 and Step 4 by using the weather data from RETScreen. • Step 6: By comparing the results from Steps 4 and 5, make a final assessment of the prediction performance of the advanced CBR model.

∑t = 0 (1 +t r)t ∑t = 0 (1 +t r)t

(16)

where, SIR is the saving to investment ratio; St is the saving in year t; It is the investment in year t; r is the real discount rate; and n is the period of the life cycle analysis. 2.6. Stochastic Approach. The MADSR estimation model proposed in this study can be used in the planning phase of a project where there is insufficient information on the project. There always exists uncertainty in climatic and geographical information, which is used as independent variables of the MADSR estimation model. Therefore, a stochastic approach that considers various kinds of uncertainty is required.88,89 To consider various kinds of uncertainty related to the proposed advanced CBR model, this study conducted MCS using the software called Crystal Ball. Assumptions for MCS can be generally divided into two types: (i) Assumption A: the uncertainty to the prediction accuracy of the proposed MADSR estimation model. Here, uncertainty was defined by a normal distribution using the average and the standard deviation of prediction accuracy; (ii) Assumption B: the uncertainty to the climatic and geographical information as well as simulation errors. Here, uncertainty was defined by a normal distribution by using the average annual error rate between the actual electricity generation and simulation-based electricity generation, which is 5.30%. By considering unusual weather phenomena, such as the rainy seasons or typhoons in the summer, and heavy snow in the winter, this study left room for electricity generation in January, February, June, July, August, November, and December. A more detailed explanation on the assumptions was given in SI Table S4.

4. RESULTS AND DISCUSSION 4.1. Distribution of Daily Solar Radiation in South Korea. Figure 2 and SI Figures S1−S3 show the seasonal

3. CASE STUDY To verify the feasibility of the MADSR estimated by the proposed model, a case study was conducted by selecting an educational facility, “S” elementary school, in which the PV system was implemented. “S” elementary school, located in Seongbuk-gu, Seoul, introduced the PV system of 44 kW in December 2009 to substitute 7.5% of the annual energy consumption. SI Table S5 shows the facility characteristics and energy consumption of “S” elementary school. Meanwhile, the PV system is categorized by the solar cell materials into crystalline silicon solar cells and amorphous silicon cells. In the Korean PV system market, the monocrystalline solar module and the multicrystalline module are the most widely commercialized among the crystalline silicon solar cells. “S” elementary school introduced the PV system with 200 W multicrystalline modules. Through such preliminary information and market research, this study selected the PV system’s panel and inverter that will be applied in the simulation (refer to SI Table S6). By using the advanced CBR model proposed in this study, the MADSR in the location of the “S” elementary school (205, Seokgwan-dong, Seongbuk-gu, Seoul, Korea) was estimated. For example, SI Table S7 presents the prediction results on the MADSR in October 2011 by using the advanced CBR model. A total of six similar cases were retrieved, and the average value of the six retrieved cases was 3.5 kWh/m2/day. Since the average prediction accuracy of the advanced CBR model is 95.69%,

Table 2. Comparison of Prediction Accuracy and Standard Deviation by Model type of model

methodology

the average prediction accuracy

the standard deviation of the prediction accuracy

annually

MRA ANN CBR advanced CBR

83.83 87.29 84.24 86.21

12.96 11.67 16.69 11.46

monthly

MRA ANN CBR advanced CBR

95.32 95.65 91.36 95.69

4.50 4.23 7.55 3.67

average values of the MADSR observed from March 2011 to February 2012 in 24 regions in South Korea using the software program called ArcGIS 9.3. Since South Korea belongs to a temperate monsoon climate region with four distinctive seasons, its climate shows high temperature and humidity in the summer due to the effect of the southeast monsoon, while in the winter, it is cold and dry due to the northwest monsoon. It is a peninsula with three sides surrounded by the sea, and 4834



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Table 3. Monthly Comparison of Prediction Accuracy and Standard Deviation by Modela methodology MRA ANN CBR advanced CBR a

APA SDPA APA SDPA APA SDPA APA SDPA

Jan.

Feb.

Mar.

Apr.

May

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

Average

93.60 6.61 93.41 7.08 83.96 13.63 94.02 6.22

95.34 3.77 95.71 3.55 91.09 6.73 95.42 3.22

96.02 3.74 96.55 3.01 93.33 6.26 96.46 2.58

96.45 3.07 96.70 2.83 93.53 5.09 96.65 2.62

96.50 2.67 97.32 2.24 94.25 5.23 97.16 2.12

95.47 3.52 95.98 3.31 92.55 5.83 95.99 3.04

94.26 4.28 94.98 4.41 90.42 8.04 94.77 3.42

95.33 3.98 95.34 3.78 91.06 6.95 95.42 3.33

96.30 3.18 96.66 2.79 92.41 6.47 96.58 2.34

95.96 4.12 95.89 4.35 92.47 6.72 96.08 3.82

94.49 7.53 94.72 7.07 91.35 8.83 94.72 7.20

94.09 7.50 94.48 6.38 89.95 10.79 95.04 4.12

95.32 4.50 95.65 4.23 91.36 7.55 95.69 3.67

Note: APA stands for the average prediction accuracy (%); and SDPA stands for the standard deviation of the prediction accuracy (%).

Table 4. LCC and LCCO2 Analysis Resultsa classification

class 1

class 2

initial construction cost ($) government subsidy ($) initial investment cost ($) operation & maintenance cost ($) replacement cost ($) repair cost ($) electricity generation benefit ($) electricity savings ($) CO2eq. savings from electricity generation ($) net present value(NPV40) ($) saving-to-investment ratio(SIR40) (%) break-even-point(BEP) (year) electricity generation (kWh)

122 201 48 880 73 321 69 671 56 047 13 624 549 160 539 587 9573 406 169 3.841 5 55 546

122 201 48 880 73 321 69 671 56 047 13 624 535 223 526 841 8382 392 232 3.743 6 48 637

from interviews with the MADSR specialists in the KMA and KIER: (i) monthly mean percentage of sunshine; (ii) monthly mean cloud amount; and (iii) monthly mean temperature. As shown in SI Table S8, the correlation coefficient between the MADSR and monthly mean temperature in the spring was very high at 0.744 (generally, if the absolute value of a correlation coefficient is over 0.5, the strength of the relationship is considered to be strong).90 Since South Korea is located in the northern hemisphere, the temperature increases as one goes south. The MADSR distribution in the spring, as presented in Figure 2, shows the increase in the MADSR going south, from which it can be determined that the MADSR is affected by temperature. Refer to Figures S1−S3 in the SI for a more detailed explanation on daily solar radiation by region for the summer, the fall and the winter season in South Korea. As shown in Figure 2 and SI Figures S1−S3, the MADSR distribution in South Korea shows a very diverse pattern due to its climatic and geographical characteristics. Furthermore, local characteristics sometimes resulted in a case different from the overall pattern of the MADSR distribution. For example, Jeju, the largest island in South Korea, exhibits an oceanic climate, whereas the other regions showed a continental climate. Moreover, the MADSR patterns in the typical erosion basins such as Daegu and Wonju were somewhat different from that of the neighboring regions. Based on such facts, it can be seen that it would be very difficult to accurately estimate the MADSR in

a

Note: CO2 conversion factor = 0.4716 tCO2/MWh, KCER = US $10.32/tCO2, the exchange rate (KRW/USD) is 1158.5 won to a U.S. dollar (as of 26 June 2012).

70% of its land consists of mountains, centered on the Taebaek Mountains that runs long from the north to the south, with high east and low west. Due to such climatic and geographic characteristics, South Korea shows diverse MADSR distributions in each season. Through the correlation analysis result (refer to SI Table S8) between the MADSR and main meteorological factors, the overall causal relationship can be determined. The main meteorological factors were selected

Figure 3. Probability distribution graph based on Assumption A and B. 4835



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PV system’s panel and inverter information. As shown in SI Table S5, “S” elementary school introduced the 44 kW PV system in December 2009. By considering the installation angle of the solar panel as 30°, the recommended angle by Seoul City, a total of 220 solar panels are required to generate 44 kW of electricity.91,92 Here, the installation area of a solar panel can be calculated by SI Figure S4. This study calculated that the installation area of a solar panel is about 2.5 m2. Therefore, the total installation area was calculated to be about 550.79 m2. As shown in SI Table S5, since the roof area of “S” elementary school was 1671 m2, it was determined that installation is possible. SI Table S9 shows the result derived from the aforementioned verification process, which compared the actual and simulation-based electricity generation. (The result was divided into Class 1, the simulation based on the MADSR estimated by the advanced CBR model, and Class 2, the simulation based on the weather data offered by RETScreen. Class 2 was used as the comparison index to Class 1.) To verify the feasibility of the result of this study, four assessment indices were used as follows: CV(RMSE), RMSE, MAE, and MAPE. The result of the analysis on the four assessment indices is as follows: first, in Class 1 and Class 2, CV(RMSE) was shown to be 9.18% and 15.97%. In both classes, CV(RMSE) was within 25%, the limit of the error, showing the feasibility of energy simulation. Likewise, CV(RMSE) of Class 1 was lower than that of Class 2; second, Class 1 showed 403.71% of RMSE, 116.54% of MAE, and 6.99% of MAPE, while Class 2 showed 702.19%, 202.71%, and 11.84%. In all three of them, Class 1 was lower than Class 2. In summary, the result of the analysis of Class 1 was shown to be superior to that of Class 2. Based on such results, it is determined that the proposed advanced CBR model will estimate electricity generation more accurately from the installation of the PV system. 4.4. LCC and LCCO2 Analysis Results. Table 4 shows the LCC and LCCO2 analysis results on Class 1 and Class 2. Since the PV system’s type and installation specifications are identical to each other, the initial construction cost and the operation and maintenance cost were identical. However, as shown in the shaded area of Table 4, the prediction results on the MADSR were different, thus, resulting in different electricity generation benefits. The prediction accuracy of Class 1 was shown to be superior to that of Class 2, based on which the following can be determined. In case of using the proposed advanced CBR model (Class 1), NPV40, SIR40, and BEP were US$406,169, 3.841, and five years, respectively. When the weather data offered by RETScreen (Class 2) were used, NPV40, SIR40, and BEP were US$ 392,232, 3.743, and six years, respectively. It is determined that the decision-making based on the analysis result of Class 1 shows a more positive outlook compared to that of Class 2. The proposed model could be useful for owner or construction manager in charge of determining whether or not to introduce the PV system and where to install it. Also, it would benefit contractors in a competitive bidding process to accurately estimate the electricity generation of the PV system in advance and to conduct an economic and environmental feasibility study from the life cycle perspective. Since the proposed model was developed using Microsoft Excel’s VBA, the users could correctly, quickly, and easily find useful results by entering geographical and meteorological factors as an independent variable and improve the prediction performance via the continuous database accumulation.

regions where there are no MADSR observation data. Consequently, it is not reasonable to measure the MADSR in all regions. Therefore, it is very important to improve the prediction accuracy of a MADSR estimation model that this study is aiming to develop. This can ultimately maximize the production efficiency of the PV system. 4.2. Comparison of Prediction Accuracy by Model. The feasibility of the advanced CBR model was verified. This study used both the ANN model, which is usually used in previous studies, and the MRA model based on the statistical theory as a MADSR estimation model. This study also used a CBR model as the direct comparison index for improving the advanced CBR model. Table 2 shows the average prediction accuracy of ANN, MRA, CBR, and the advanced CBR model as well as the standard deviation of the prediction accuracy. It shows that the 12 clusters formed by month were effective. Through cluster formation, the average prediction accuracy by model was shown to have improved between 7.12% and 11.49%, and the standard deviation of the prediction accuracy was improved between 7.44% and 9.14%. It shows that cluster formation significantly improved prediction performance. Table 3 shows the average prediction accuracy and the standard deviation of prediction accuracy of the 12 monthly based MADSR estimation model. First, the average prediction accuracy of the advanced CBR model was very high at 95.69%, and the standard deviation of the prediction accuracy was 3.67%, showing a significant improvement in prediction accuracy and consistency. Compared to the other models, the improvement in the advanced CBR model can be assessed in the following manner. First, compared to the MRA model, the advanced CBR model improved in all aspects; then by month, its average prediction accuracy improved by 0.08−0.95%, and the standard deviation of the prediction accuracy by 0.30− 3.38%. On the average, its average prediction accuracy improved by 0.38%, and the standard deviation of the prediction accuracy by 0.83%. Second, compared to the ANN model, the average prediction performance of the advanced CBR model was slightly better. On average, its average prediction accuracy and the standard deviation of the prediction accuracy improved by 0.05% and 0.56%, respectively. Third, compared to the CBR model, the advanced CBR model improved in all aspects. By month, its average prediction accuracy and the standard deviation of the prediction accuracy improved by 2.91−10.06% and 1.63−7.41%, respectively. On average, its average prediction accuracy and the standard deviation of the prediction accuracy improved by 4.33 and 3.88%, respectively. In conclusion, it was determined that the advanced CBR model obtained the advantage of the ANN model and also showed the explanatory power on the prediction results by referencing historical cases. That is, the proposed model improved the disadvantage of the ANN model, which has “black-box” that prevents users from understanding prediction results. Accordingly, the proposed model can help users to estimate the MADSR more accurately as well as to understand the prediction results more easily, and thus this will help them conduct the conceptual design and analysis of the PV system more efficiently. 4.3. Validation of the Simulation Results. To calculate simulation-based electricity generation, it is necessary to determine the installation angle of the solar panel and the number of solar panels installed along with the aforementioned 4836



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4.5. Stochastic Analysis Using MCS. In MCS operation, Assumption A and Assumption B were all defined, and after 5000 simulations, the probability distribution graph to the annual electricity generation, as shown in Figure 3, was extracted. SI Table S9 shows that the actual electricity generation was 52 750 kWh, while the simulation-based electricity generation was 55 546 kWh. These values were obtained using the deterministic approach. On the other hand, Figure 3 shows the results from a stochastic approach where the simulation-based electricity generation resulted in 54 026 kWh. The result (54 026 kWh) from the stochastic approach was closer to the actual electricity generation (52 750 kWh) than the result (55 546 kWh) obtained using the deterministic approach. By showing the results along with the probabilistic distribution graph, the proposed model helped the final decision-maker consider various options.

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ASSOCIATED CONTENT

S Supporting Information *

Detailed data on the monthly average daily solar radiation in South Korea, which were used in this study. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 82-2-2123-5788; fax: 82-2-2248-0382; e-mail: hong7@ yonsei.ac.kr. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2012-004376 and No. 2012-0001247).

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REFERENCES

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Estimation of the Monthly Average Daily Solar Radiation using

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