Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model

Renewable Energy 75 (2015) 675e693

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Renewable Energy journal homepage: www.elsevier.com/locate/renene

Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model Amit Kumar Yadav, S.S. Chandel* Centre for Energy and Environment, National Institute of Technology, Hamirpur, Himachal Pradesh, India

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 July 2014 Accepted 18 October 2014 Available online

Solar potential of western Himalayan Indian state of Himachal Pradesh is assessed using Artificial Neural Network (ANN) based global solar radiation (GSR) prediction model. J48 algorithm in Waikato Environment for Knowledge Analysis (WEKA)is used for the selection of input parameters for ANN model for predicting GSR. Most relevant input parameters are found to be temperature, altitude and sunshine hours whereas latitude, longitude, clearness index and extraterrestrial radiation are found to be least influencing variables. The usefulness of J48 algorithm in variable selection is checked by developing five ANN models: ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5. The maximum mean absolute percentage error (MAPE) for ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5 are found to be 16.91%, 16.89%, 16.38%, 6.89% and 9.04% respectively. ANN-5 model is used to develop the solar maps of Himachal Pradesh. The estimated GSR varies from 3.59 to 5.38 kWh/m2/day indicating good solar potential for solar energy applications. A correlation is developed between NASA satellite data and ground measured GSR data to find values close to ground measured GSR for different locations. The correlation coefficient is found to be 0.97. Models developed can be used to assess solar potential of any location worldwide. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Solar potential Global solar radiation Artificial neural network J48 algorithm Western Himalayas

1. Introduction The Indian state of Himachal Pradesh (H.P) is located between 30.38
to 33.21
North latitudes and 75.77
e79.07
East longitudes, in Western Himalayas with geographical area of 55,673 km2. Over 90% of population lives in rural areas. The elevation of H.P varies from 250 m to 6500 m above sea level state and extends from Shivalik hill ranges (600 m) to Dhauladhar mountain range (4550 m) and Great Himalayan range (5000 me6000 m) [1]. The elevation increases from west to east and from south to north. The average summer temperature of the state ranges between 25
Ce30
C. The low temperatures and dust free clean environment has good potential for power generation using solar photovoltaic route. The monthly average temperature decreases with altitude and shows a regional difference of approximately 15
C from higher elevations (above 3500 m) to lower (below 1000 m). Hydropower is the main source of electricity in H.P. It is estimated that 21,244 MW of hydropower can be generated in the state by

* Corresponding author. Tel.: þ91 9418011957; fax: þ91 1972 223834. E-mail addresses: [email protected], [email protected] (S.S. Chandel). http://dx.doi.org/10.1016/j.renene.2014.10.046 0960-1481/© 2014 Elsevier Ltd. All rights reserved.

constructing various hydro project on river basin. At present the total hydro potential of H.P. is 3934.74 MW, out of which 7.6% is under control of H.P. government and the rest is under central government. The major hydro project is on river Sutlej is the Nathpa Jhakri which generates nearly 1500 MW electricity. The major problem associated with hydro plants in HP is economical, financial, social, environmental and geographical. In addition in winter season due to shortage of water sufficient power cannot be produced and the state has to purchase power from other states. Therefore it is worthwhile to utilize availability of solar energy in the state. The reliable measured data are not available for the region except one location i.e. Centre for Energy and Environment (CEE), National Institute of Technology, Hamirpur which has meteorological station. The continuous meteorological variables at one minute time interval (air temperature, relative humidity, wind speed, wind direction, rainfall and solar radiation) are measured at Centre for Energy and Environment meteorological station. Earlier a few studies have been conducted for the estimation of solar radiation in western Himalayan region by Chandel and Aggarwal [2] and Chandel et al. [3,4] using conventional methods. Ramachandra et al. [5] and Krishnadas and Ramachandra [6] have estimated the solar energy potential of Himachal Pradesh using National Aeronautics and Space Administration (NASA) database

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solar technologies for a region. The objective of the present study is to assess the solar potential of western Himalayan Indian state of Himachal Pradesh using Artificial Neural Network (ANN) based GSR prediction model. The paper is organized as follows: The Section 2 presents an overview of research on global solar radiation prediction using different input variables for ANN models. The data used and methodology are given in Section 3. The results are presented and discussed in Section 4 and conclusion in Section 5.

Nomenclature CI ER GSR H Lat Long LM MAPE MBE n R RMSE SH SRiðANNÞ SRiðactualÞ T Tmax Tmin

clearness index extraterrestrial radiation (kWh/m2/day) global solar radiation (kWh/m2/day) altitude (m) latitude (
N) longitude (
E) LevenbergeMarquardt mean absolute percentage error (%) mean bias error no. of data samples correlation coefficient root mean square error (kWh/m2/day) sunshine hours (h) predicted monthly average daily global solar radiation data for i month (kWh/m2/day) measured monthly average daily global solar radiation data for i month (kWh/m2/day) temperature (
C) maximum temperature (
C) minimum temperature (
C)

2. Overview of most influencing input parameters for ANN based solar radiation prediction

values for global solar radiation. NASA measured values of GSR data are available but it lacks accuracy and root mean square error between NASA solar data and ground measured solar radiation data are shown to vary for Indian locations from 0.177 to 0.416 kWh/m2 [7,8]. In Himachal Pradesh, the Centre for Energy and Environment, National Institute of technology Hamirpur, Himachal Pradesh India has a solar radiation monitoring facility since 2009. Thus there is a need to explore the solar power potential of the state for which detailed solar resource assessment has to be carried out. The estimation of solar power potential is important to identify suitable

Solar radiation is one of the most important parameters for a number of solar energy applications. The solar radiation is not available at most of site so different forecasting techniques are used [9,10]. The prediction of solar radiation using ANN models incorporates several meteorological and geographical parameters as input variables. To select most influencing relevant input variables for ANN models researchers have to use combinations of input variables for solar radiation prediction which is time consuming. Rehmana and Mohandes [11] used three combinations of input variables (day of the year, air temperature and relative humidity) in ANN to estimate GSR for Abha city in Saudi Arabia. The mean absolute percentage error (MAPE) is 4.49% for input combination (relative humidity, daily mean temperature), MAPE is 11.8% for input day of the year, mean temperature and MAPE is 10.3% for input maximum temperature. Behrang et al. [12] predict daily GSR for Dezful city in Iran using different ANN techniques based on different combination of meteorological variables (day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed). The MAPE for the Multilayer Perceptron (MLP) network is 5.21% while this value is 5.56% for Radial Basis Function (RBF) network. Alam et al. [13] used sixteen different combinations of input variables latitude, longitude, altitude, time, months of the year, air

Table 1 Input annual average meteorological data and geographical variables for ANN model. S. no. Training data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Testing data 1 2 3 4

Cities

Lat (
N)

Long (
E)

H (m)

T (
C)

Tmax (
C)

Tmin (
C)

SH (h)

ER

CI

Srinagar New Delhi Jodhpur Jaipur Varanasi Patna Shillong Bhopal Ranchi Bhavnagar Nagpur Mumbai Pune Hyderabad Vishakapatnam Panjim Chennai Port Blair Minicoy Thiruvanathpuram Dehradun Lucknow

34.08 28.35 26.18 26.92 25.45 25.61 25.34 23.25 23.35 21.77 21.09 19.07 18.52 17.36 17.43 15.49 13.081 11.61 8.28 8.5 30.19 26.45

74.79 77.12 73.01 75.82 82.85 85.13 91.53 77.42 85.33 72.15 79.07 72.51 73.84 78.46 83.14 73.81 80.27 92.72 73.03 76.9 78.02 80.53

1730 216 224 431 81 53 1598 523 654 24 311 14 560 536 3 7 6 73 2 64 683 128

5.3 23.8 25.1 24.6 25.2 24.7 22.5 26 24.3 28.3 26.5 26.7 25.2 27 26.6 26.6 27.7 27.3 27.2 26.9 11.3 25.1

15.8 31.7 31.2 31.5 31.4 28.7 26 34 29.1 32.2 34.5 28.1 27.5 31.7 28.7 27.6 30.1 28.1 28.1 27.6 18.6 32.1

6.7 13.4 15.8 15.1 16.8 17.2 16.1 18.6 17.3 24.2 20 24.7 23.8 23.2 23.8 25.3 25.1 26.4 26.7 26.2 1.7 15.8

6.23 7.74 8.84 8.05 8.02 8.33 5.49 8.1 7.92 8.46 7.83 7.73 7.73 7.85 7.86 7.78 7.76 7.74 7.68 7.87 7.85 7.84

8.43 8.87 9.04 9.06 9.10 9.09 9.09 9.24 9.23 9.33 9.37 9.47 9.50 9.56 9.54 9.33 9.32 9.79 9.88 9.81 8.74 9.01

0.56 0.61 0.67 0.65 0.62 0.62 0.49 0.61 0.62 0.63 0.59 0.59 0.60 0.60 0.58 0.57 0.55 0.46 0.51 0.59 0.59 0.62

Hamirpur Ahmedabad Bangalore Kolkatta

31.68 23.04 12.57 22.39

76.52 72.38 77.38 88.27

785 169 897 6

15.9 27.4 24.7 25.7

24 32.2 27.5 28.4

6 24.2 21.9 20.2

7.67 7.78 7.79 7.78

9.31 9.25 9.74 9.28

0.56 0.63 0.55 0.54

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Fig. 2. ANN-1 model sensitivity analysis.

Fig. 1. J48 algorithm in WEKA for relevant input variables in ANN models.

temperature, relative humidity, rainfall, wind speed and net long wavelength to estimate hourly and daily diffuse solar radiation for Indian stations. The maximum root mean square error (RMSE) for  pez et al. [14] used automatic relevance ANN model is 8.8%. Lo determination (ARD) method for finding most influencing input variable to ANN in modeling of direct solar irradiance. The ARD method shows that clearness index and relative air mass are relevant input variables for estimating hourly direct solar irradiance and ARD method can be used for locations where limited meteorological variables are available. Bosch et al. [15] also used ARD to select ANN inputs for estimation of daily global irradiation on stations located in complex terrains. The forward selection pruning method is used for identifying relevant variables for prediction [16,17]. The hyper parameters associated with longitude, latitude are large showing less relevant of these parameters as inputs and

for altitude, slope, azimuth angle, day of year, daily extraterrestrial solar radiation and clearness index hyper parameters are small showing more relevant input parameters. The ARD methodology shows that site altitude is most important parameter for solar radiation estimation over a complex terrain. The RMSE is found to be 6.0%. Will A et al. [18] used Niching Genetic Algorithms (NGA) to select most significant input parameters for estimation of solar n, Argentina). The NGA involves radiation in El Colmenar (Tucuma codification of problem, assigning limit to each variable, finding out distance function in the search space, crossover operators and application ratios of crossover operators, initial population size and number of generations. The data from 14 stations spread in North of Argentina are used as input and results show that NGA estimates climatic variables from database containing missing data. The RMSE, R are 2.36 MJ/m2, 0.926 for 70 individuals/85 generations and for 200 individuals/150 generations RMSE, R are 2.34 MJ/m2, 0.928 respectively. This algorithm can be used to analyze more number of data at the same time and is useful in selecting relevant stations and variables for solar radiation estimation. Rumbayan et al. [19] used MLP to develop solar radiation map for Indonesia. The inputs are average temperature, average relative humidity, average sunshine duration, average wind speed, average precipitation, longitude, latitude and month of the year. The MAPE is 3.4% with 9 neurons in hidden layer. Ouammi et al. [20] developed ANN model for mapping monthly GSR of 41 Moroccan sites. The model incorporates longitude, latitude and elevation. The variation in

Table 2 Rank of input variables by J48 algorithm for GSR prediction. Month

Temp.

Min. T

Max. T

H

SH

CI

ER

Lat.

Long.

Jan Feb March April May June July Aug. Sep. Oct. Nov. Dec.

0.139 0.138 0.146 0.097 0.086 0.055 0.023 0.024 0.095 0.061 0.115 0.104

0.124 0.124 0.128 0.089 0.081 0.040 0.023 0.024 0.029 0.052 0.096 0.094

0.097 0.097 0.111 0.074 0.049 0.027 0.021 0.021 0.027 0.048 0.080 0.072

0.084 0.074 0.082 0.069 0.038 0.023 0.021 0.019 0.010 0.045 0.057 0.069

0.075 0.071 0.073 0.053 0.031 0.014 0.014 0.012 0.001 0.043 0.054 0.051

0.073 0.063 0.070 0.047 0.022 0.011 0.014 0.007 0.003 0.042 0.043 0.047

0.059 0.047 0.062 0.041 0.011 0.010 0.001 0.003 0.006 0.016 0.040 0.047

0.004 0.005 0.030 0.040 0.010 0.005 0.008 0.002 0.0108 0.014 0.0111 0.008

0.016 0.015 0.023 0.030 0.001 0.001 0.015 0.003 0.019 0.006 0.011 0.013

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Fig. 5. ANN-4 model sensitivity analysis.

Fig. 3. ANN-2 model sensitivity analysis.

solar energy potential of vacant waste land in H.P. In addition the correlation between NASA measured GSR and ground measured GSR by pyranometer is developed which can be used to determine NASA measured GSR close to ground measured GSR.

predicted GSR is 5030e6230 Wh/m2/day which is important for selection of solar power generation sites. Yadav and Chandel [21] presented a comprehensive review of solar radiation prediction using ANN techniques and identified the selection of appropriate meteorological and geographical variables as input to ANN models as a major research gap which require to be addressed. In the follow up study Yadav et al. [22] have used Waikato Environment for Knowledge Analysis (WEKA) for the selection of relevant input variables amongst latitude, longitude, temperature, maximum temperature, minimum temperature, altitude and sunshine hours and found that latitude and longitude are less influencing parameters in solar radiation prediction. In the present study two additional variables namely extraterrestrial radiation and clearness index are considered along with latitude, longitude, temperature, maximum temperature, minimum temperature, altitude, sunshine hours. The J48 algorithm is implemented using WEKA software version 3.7.10 for the selection of most relevant input parameters to ANN model. The input variables identified are then used for the prediction of GSR for H.P. cities to develop solar radiation map and calculating

The 26 Indian cities from different climatic zones are selected for training and testing of ANN models for predicting GSR over HP cities, as shown in Table 1. The average temperature T, Maximum Tmax and minimum Tmin for twenty two year period of different locations are taken from Langley Research Center of National Aeronautics and Space Administration (NASA) [23]. The sunshine hour and monthly average daily GSR data (kWh/m2/day) at for Hamirpur are measured at the automatic solar radiation station of Centre for Energy and Environment, National Institute of Technology, Hamirpur H.P. India. The GSR data for Hamirpur is continuously measured at time step of one minute by KIPP and ZONEN CM

Fig. 4. ANN-3 model sensitivity analysis.

Fig. 6. ANN-5 model sensitivity analysis.

3. Methodology 3.1. Global solar radiation database

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where Isc is solar constant 1367 W/m2 and n is the Julian day.

Table 3 ANN models prediction accuracy. Models Input variables

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Testing cities maximum MAPE (%)

Highlights

16.91

Good prediction accuracy Good prediction accuracy Good prediction accuracy High prediction accuracy High prediction accuracy

ANN-1 T, Tmin, Tmax, H, SH, CI, ER, Lat and Long ANN-2 T, Tmin, Tmax, H, SH, CI and ER

16.89

ANN-3 T, Tmin, Tmax, H, SH and CI

16.38

ANN-4 T, Tmin, Tmax, H and SH

6.89

ANN-5 T, Tmin, Tmax and H

9.04

CI ¼

ER

(2)

3.2. The J 48 algorithm

4 high temperature pyranometer. The GSR for other 23 cities for period 1986e2000 is taken from solar radiation handbook [24] which is contributed by joint project of solar energy centre part of Ministry of New and Renewable Energy (MNRE) and Indian Metrological Department (IMD) Pune. The thermoelectric pyranometer is used by IMD for measuring daily GSR in the range 300e4000 nm. The GSR of Lucknow and Dehradun are taken from IMD Pune compiled by Anna Mani [25]. The average value of Extraterrestrial global solar radiation (ER) and Clearness Index (CI) for cities are calculated using following equations

ER ¼ Isc ½1 þ 0:333 cosð360n= 365Þ

SRiðactualÞ

(1)

J48 algorithm is a modified version of c4.5 and ID3 algorithm which is used to construct the decision trees [26]. The decision tree uses tree like graph and acts as decision support system. In decision tree the internal node denotes test on attribute, branch signify outcome of test and leaf node denotes class label (computational analysis of all attributes). The path from roof to leaf is called classification rules. The tree consists of decision nodes, chance nodes and end nodes which are denoted by squares, circles and triangles. The standard tree is generated with c5.0, ID3 and c4.5 algorithm consist of number of branches, one root, number of nodes and leaves. At each node in the decision tree, the estimation criteria are used to select relevant input variables for prediction. The estimation criteria are used to identify input variables and are based on entropy reduction and information gain [27]. The entropy (E) is given by following Equation (3) in which pP; pN are proportion of positive, negative (training) examples.

E ¼ pP  log2 ðpPÞ  pN  log2 ðpNÞ

(3)

The J48 algorithm is implemented through Waikato Environment for Knowledge Analysis (WEKA) software for selecting

Fig. 7. GSR map of HP in January.

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relevant input parameters to ANN based GSR prediction model. WEKA is freely available software which is developed at the University of Waikato in New Zealand. WEKA presents implementation of machine learning algorithm and data mining. The user can perform regression, classification, clustering, association, visualization etc using WEKA. The regression is used for finding function to model data with minimum error, classification put data of similar types in one class, clustering create and assign group of similar data. 3.3. Selection of relevant input parameters for ANN models The input variables: temperature, minimum temperature, maximum temperature, altitude, sunshine hours, clearness index, extraterrestrial radiation, latitude and longitude of different cites and GSR of all months is used in WEKA graphical user interface (GUI) (Fig. 1). The rank of input variables as given by the use of J48 algorithm in WEKA GUI is shown in Table 2. It is found that clearness index, extraterrestrial radiation, latitude and longitude are having lower rank, showing these are least influencing variables. In order to check the authentication of J48 algorithm for relevant input variable selection five ANN models (ANN-1, ANN-2, ANN-3, ANN-4 and ANN5) are developed and GSR prediction accuracy is evaluated.

2, ANN-3, ANN-4 and ANN-5) are developed using MATLAB software (R 2011a). The ANN-1 model utilized T, Tmin, Tmax, H, SH, CI, ER, Lat and Long. ANN-2 model used T, Tmin, Tmax, H, SH, CI and ER as input parameters. The ANN-3 used inputs as T, Tmin, Tmax, H, SH and CI. The ANN-4 used inputs as T, Tmin, Tmax, H and SH. ANN-5 model utilized T, Tmin, Tmax, H and it is used for prediction where no sunshine duration data are found. The LevenbergeMarquardt (LM) algorithm is used for training the ANN models. The number of hidden layer neurons is calculated by Equation (4) [28,29], where Hn and Sn are number of hidden layer neurons and number of data samples used in ANN model, In and On indicate number of input and output variables.

Hn ¼

In þ On pffiffiffiffiffi þ Sn 2

The sensitivity test is carried out for validating the number of hidden layer neurons by calculating change in prediction error (MAPE) when number of hidden layer neurons is changed by ±5 from calculated hidden layer neurons using Equation (1). The sensitivity analysis of hidden layer neurons for ANN models are shown in Figs. 2e6. The MAPE is shown in equation (5) and ANN architecture with least MAPE is used for prediction of GSR.

3.4. Prediction using ANN models The relevant input parameters for ANN models are decided using rank given in Table 2. Therefore five ANN models (ANN-1, ANN-

(4)

MAPE ¼

n SR 1X iðANNÞ  SRiðactualÞ n i¼1 SRiðactualÞ

Fig. 8. GSR map of HP in February.

!  100

(5)

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3.5. Global solar radiation mapping of Himachal Pradesh e case study ArCGIS 9.1 is used to map the monthly GSR of Himachal Pradesh. The geographical coordinate system with WGS84 as datum is selected as coordinate system for mapping. The monthly GSR data predicted from ANN-5 model for 13 cities have been used to prepare GIS database. First, point features are created using these data with solar radiation of each month as attributes. Another layer of Himachal Pradesh with district boundaries as polygon features is extracted from India administrative boundary layer [30]. A new polygon feature is created by spatially joining both point and polygon features created earlier. The attributes belonging to same polygon during overlay process are averaged for developing solar radiation map of each month. 4. Results and discussion The prediction accuracy of ANN models is calculated with MAPE as suggested by Lewis [31]. The MAPE 10% shows high prediction accuracy, 10%  MAPE  20% shows good prediction, 20%  MAPE  50% specified reasonable prediction, MAPE 50% indicates inaccurate forecasting as shown in Table 3. It is clear from Table 3 that ANN-4 and ANN-5 model show higher prediction accuracy than other ANN models identifying T, Tmin, Tmax, H and SH as the most relevant input variables for GSR prediction. In addition T, Tmin, Tmax and H can be used as input

681

variables in ANN models for sites where sunshine hour data are not available. The least influencing variables are Lat, Long, CI and ER. Therefore J48 algorithm in WEKA can be used to find relevant input variables for GSR prediction. 4.1. Annual and monthly global solar radiation maps The sunshine hours data are not available for most of cities in Himachal Pradesh so ANN-5 model is used for prediction of GSR. The predicted GSR values from ANN-5 model are presented in the form of monthly maps to obtain spatial database. The GSR values are classified as low <4.50 kWh/m2/day), medium between 4.50 and 6 kWh/m2/day and high >6 kWh/m2/day. The dark color in map highlights maximum solar energy potential in that location. In January all parts of H.P. receive low GSR [Fig. 7]. The H.P. cities (Chamba, Kangra, Una, Hamirpur, Bilaspur, Solan, Sirmaur, Mandi and Kullu) receive medium range of GSR in February and March [Figs. 8 and 9]. In April Lahaul Spiti, Kullu, Shimla and Kinnaur receive medium range of GSR whereas rest of H.P. cities receive high range of GSR [Fig. 10]. In May only Shimla receives medium range of GSR whereas rest of H.P. cities receives high range of GSR [Fig. 11]. In June Kangra, Chamba, Lahaul and Spiti, Kullu and Kinnaur receive high range of GSR whereas rest of H.P. cities receives medium range of GSR [Fig. 12]. In July except Sirmaur (low range) all cities of H.P. receives GSR of medium range [Fig. 13]. In August Chamba, Kangra, Kullu, Kinnaur receives medium range of GSR and rest other cities have GSR of low range [Fig. 14]. In September and October month

Fig. 9. GSR map of HP in March.

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Fig. 10. GSR map of HP in April.

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Fig. 11. GSR map of HP in May.

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Fig. 12. GSR map of HP in June.

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Fig. 13. GSR map of HP in July.

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Fig. 14. GSR map of HP in August.

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Fig. 15. GSR map of HP in September.

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Fig. 16. GSR map of HP in October.

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Fig. 17. GSR map of HP in November.

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Fig. 18. GSR map of HP in December.

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Fig. 19. Annual GSR map of HP.

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Table 4 District wise solar energy potential of Himachal Pradesh. District

Lat (
N)

Long (
E)

Annual GSR (kWh/m2/day)

Total geographical area (km2)

Waste land area (km2)

Solar energy potential of vacant waste land (kWh/day)

Solar energy potential of districts (kWh/day)

Chamba Kangra Una Hamirpur Bilaspur Solan Sirmaur Shimla Mandi Kullu Kinnaur Lahaul Spiti Total

32.33 32.83 31.86 31.86 31.31 30.08 31.37 31.10 31.71 31.95 31.65 32.56

76.10 76.30 76.40 76.30 76.80 77.33 77 77.21 76.96 77.10 78.47 77.45

5.209 5.383 4.594 4.778 4.653 5.271 5.210 3.594 5.087 4.778 3.885 3.948 56.39

6528 5739 1549 1118 1167 1936 2825 5131 3951 5503 6401 13,833 55,681

992.87 792.43 253.81 356.15 242.61 307.65 223.05 517.58 409.27 244.83 655.71 4085.98 9081.9

5172 4266 1166 1702 1129 1622 1162 1860 2082 1170 2547 16,131 40,009

3.4004  1010 3.0893  1010 0.7116  1010 0.5342  1010 0.5430  1010 1.0205  1010 1.4718  1010 1.8441  1010 2.0099  1010 2.6293  1010 2.4868  1010 5.4613  1010 24.1993 £ 1010

Shimla, Lahaul and Spiti, Kinnaur have low range of GSR and rest all cities have medium range [Figs. 15 and 16]. In November and December, Solan has medium range and rest of H.P. cities receives low range of GSR [Figs. 17 and 18]. The annual variation of GSR over H.P. cities varies from 3.59 to 5.38 kWh/m2/day [Fig. 19] which is in the range of values given in India solar resource map (by NREL and Solar Energy Centre) [32] and solar power potential mapping in India [33]. The annual GSR is used to assess the district wise solar energy potential of HP as shown in Table 4. The total solar energy potential of H.P. varies from 0.5342  1010 to 5.4613  1010 kWh/ day. The solar potential of vacant land of the state which can be utilized for solar power generation is calculated by neglecting various types of city wise distributed waste land areas (like permanent and seasonal types of waterlogged and marshy, shifting cultivation, under-utilized degraded forest and snow covered) from the waste land area of H.P. [34]. The vacant waste land area of H.P. has a total solar potential of 40,009 kWh/day. Lahaul &Spiti district with largest geographical area has a maximum solar potential of 16,131 kWh/day.

4.2. New correlation between ground measured global solar radiation and NASA data The ground measured, NASA measured and ANN-5 predicted global solar radiation for CEE Hamirpur site are shown in Table 5. The root mean square error (RMSE) between NASA and ground measured solar radiation is 1.3890 kWh/m2/day. The RMSE between NASA and ANN-5 predicted solar radiation is 1.6698 kWh/ m2/day. The RMSE between ground and ANN-5 predicted solar radiation is 0.4393 kWh/m2/day. The correlation of ground

Table 5 Global solar radiation values for CEE Hamirpur. Month

Ground measured value

NASA measured value

ANN-5 predicted value

Jan Feb Mar April May June July Aug Sep Oct Nov Dec

2.43 2.87 4.79 5.22 6.14 4.95 4.06 3.48 3.98 4.21 3.16 2.85

3.41 4.31 5.45 6.68 7.43 7.17 5.68 5.29 5.55 5.30 4.23 3.36

2.31 2.10 3.99 5.22 5.94 4.90 4.00 3.50 2.98 4.11 3.06 2.80

measured solar radiation value ðGSR Þ in terms of NASA measured solar radiation value ðNSR Þ is developed using curve fitting method and is given by following Equation (6) which can be used to approximate NASA satellite measured solar radiation to ground measured solar radiation. The correlation coefficient (R) value for Equation (4) is 0.97.

 GSR ¼ 0:0002ðNSR Þ6  0:0052ðNSR Þ5 þ 0:0693ðNSR Þ4  0:4892ðNSR Þ3 þ 1:9177ðNSR Þ2  3:9540ðNSR Þ  þ 3:3494  103

(6)

5. Conclusion In the present study J48 algorithm using WEKA is implemented for identifying relevant input variables for the prediction of GSR using five ANN models followed by the assessment of solar energy resource potential of the western Himalayan state of Himachal Pradesh in India. The most relevant input variables for the prediction of GSR are found to be temperature, maximum temperature, minimum temperature, altitude above mean sea level and sunshine hours. The clearness index, extraterrestrial radiation, latitude and longitude are found to be the least influencing parameters for GSR prediction. The maximum MAPE for ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5 models are found to be 16.91%, 16.89%, 16.38%, 6.89% and 9.04% respectively, showing highest accuracy of ANN-4 model which utilizes most relevant input variables as compared to other models. The ANN-4 model can be used for the prediction of GSR and hence assessment of solar energy resource potential of any location in India. A correlation between ground measured GSR values and NASA data is developed with RMSE 1.3890 kWh/m2/day and R value as 0.97 which can be used to analyze NASA satellite measured solar radiation values close to ground measured solar radiation values for different locations. The monthly and yearly GSR maps of H.P. are developed using predicted solar radiation data from ANN-5 model as sunshine hour data are not available for a large number of locations. The GSR in H.P. varies from 3.59 to 5.38 kWh/m2/day. The solar potential is found highest in Lahaul Spiti (5.4613  1010 kWh/day) and lowest in Hamirpur (0.5342  1010 kWh/day) showing good scope of solar power generation. The total solar potential of vacant waste land in the state is 40,009 kWh/day. Thus there is a vast solar energy potential in the state which can be utilized for the installation of solar photovoltaic and thermal applications.

A.K. Yadav, S.S. Chandel / Renewable Energy 75 (2015) 675e693

The solar potential is determined by considering only the waste land available in the state however the building rooftops and degraded land in rural areas present potential applications for solar power generation which can be considered in a follow up study. Future research will also be focused to find most relevant input parameters from other meteorological variables with improved prediction accuracy of different ANN models. The ANN-5 model will also be used to predict GSR and solar energy potential for the locations with measured sunshine hour data availability.

Acknowledgment The authors would like to thank Mr. Hasmat Malik and Mr Sumit Chaudhary for inputs to the present study.

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