o dimensional curves on three dimensional surfaces [16] [17] . There are two types of spline method, they are regularized method and tension method. The regularized method creates a smooth, gradually changing surface with values that can lie outside the sample data range and the tension method controls the stiffness of the surface according to the character of the modeled phenomenon. The inverse distance weighted (IDW) and spline methods are referred to as deterministic interpolation methods because they are directly based on the surrounding measured values or on specified mathematical formulas that determine the smoothness of the resulting surface. A second family of interpolation methods consists of geostatistical methods, such as kriging, which are based on statistical models that include autocorrelation, that is, the statistical relationships among the measured points. Because of this, not only do geostatistical techniques have the capability of producing a prediction surface, but they can also provide some measure of the certainty or accuracy of the predictions [18] .

The ArcGIS v10 software is used for spatial interpolation of the high resolution climate variables. The monthly average and annual measurement of above climate variables are chosen for this purpose. The ArcGIS spatial analyst toolbox was used for spatial interpolation of the climate variables. A point data file was used as inputs for spatial interpolation process based on selected variable. All the three interpolation methods, Inverse distance weighting, thin plate smoothing splines and kriging were used to interpolate clear sky insolation incident, wind speed, surface albedo and earth skin temperature variables.

3.2. Data Classification, Rating and Weight Sum Analysis

Classification of a raster data refers to the value of continuous raster cells are grouped into categories. The weight sum operation under spatial analyst toolbox has the capacity to weight and combine multiple raster data layer input to create an integrated overlay analysis.

Eight sum is similar to the weighted overlay operation with multiple raster inputs, multiple factors that can be easily combined incorporating relative importance. This operation uses a common measurement scale and weights of each variable and its subclasses as their importance [19] . Both overlay operations are very much useful for potential suitability analysis and decision support system. Limitation in the Weighted Overlay tool is the weights assigned to the input raster layers must equal to 100 percent. The advantages in weighted sum operation is the user can specify the relative weights as decimals, percentages, or relative weightings. Weighted sum operation simply works based on multiplying the chosen rank values for each input raster data layer by the specific weight. Finally, it creates an output raster layer after summing all resulted values of the individual raster layer together.

After interpolation different sets of data (for all the months and annual) were generated to represent different variables spatially. According to the degree of potential energy content to convert them into electric energy, simple statistical weighting/ratings were used for all the variables leading to a decision support approach. We contrived three rating systems, like “3” as a high potential, “2” as a moderate potential, and “1” as a low potential for all variables. Out of four variables clear sky insolation incident is most important to produce electricity using solar plate. Wind speed also very important to produce electricity through wind turbines. A weight of 5 and 3 were designed for clear sky insolation incident and wind speed respectively. All the details about weighting/rating are given in Table 2.

4. Result and Discussion

Interpolation processes yields several output files: i) a large residual file which is used to check for data errors; ii) a file that contains an error covariance matrix of fitted surface coefficients; iii) an interpolated thematic map showing the spatial distribution of the parameter. After interpolation, different sets of data (for 4 months and annual) are generated to represent different renewable energy source variables spatially. Figure 2 represents results of three interpolation techniques based on mmonthly average Earth’s skin temperature for the month of December. Spline interpolation method is highly accurate and yield smooth prediction while using uniformly-gridded data (Figure 2), but it extrapolate the estimation of values outside the range of tabulated data. Minumum, maximum, mean and standard deviation for each variable were tabulated (Table 3) based on spline interpolation technique. Surface albedo index was recorded high as 0.2070 and low as 0.0392 for the month of June and December respectively. Heights maximum Earth’s skin temperature was recorded in the month of December (30.31 ˚C) and lowest minimum in June (20.32 ˚C). Clear sky insolation incident was high in December (8.1310 kWh/m2/day) and low in June (5.0481 kWh/m2/day). Month of September experienced maximum wind speed (7.4793 m/s) significantly. Mean and standard deviations of interpolated (spline) variable were calculated for all variables as displayed in Table 3.

Table 2. Details of weighting/rating for selected variables.

Table 3. Statistics for the distribution of different variables after spline interpolation.

Figure 2. Monthly average Earth’s skin temperature for the month of December using interpolation methods (a) splining, (b) kriging, and (c) IDWA.

Result after spline interpolation for annual average clear sky insolation incident, wind speed, surface albedo and earth skin temperature variables were mapped and displayed in Figures 3(a)-(d). Interpolated annual result along the north coast of PNG was significantly lower for annual average surface albedo index (0.0406) and average wind speed (1.5396 m/s) and higher in the middle portion of the country 0.1954 and 5.9472 m/s respectively. Reverse results were cropped out with high average Earth’s skin temperature (>29˚C) and high clear sky insolation incident (>7 kWh/m2/day) along the north coast region.

All four variables were used for weight sum overlay analysis based on their significance rating and weight to find out the potential renewable energy distribution annually and monthly basis. Based on four special days in a year (summer solstice, equinox and winter solstice) four different months (December, March, June and September) were chosen to analysis potential renewable energy distribution. Weight sum overlay model had produced a

Figure 3. Spline interpolation of Annual average (a) surface albedo, (b) Earth skin temperature, (c) clear sky insolation incident and (d) wind speed.

range of spatially distributed total value ranged from 12 to 30. Higher value refers scale of potentiality of renewable energy. Output value again classified into five (5) groups as: i) very low potential (Less than 13), ii) low potential (14 to 16), iii) medium potential (16 to 20), iv) high potential (20 to 24), and v) very high potential (more than 24). Different thematic map for average potential renewable energy distribution in different months and annual is shown in Figures 4(a)-(e). In the month of March (Figure 4(a)) renewable energy distribution is very high in scale in the northern part of West and East Sepik and most areas of Manus, New Ireland, North Solomon, East New Britain, West New Britain, Northern, Central and Milne Bay Provinces of PNG. All other remaining location is characterized by high potential. Month of June (4b) is not significant with high renewable energy distribution because of winter solstice. Figure 4(c) and Figure 4(d) are representing renewable energy

Figure 4. Average potential renewable energy distribution in (a) March; (b) June; (c) September; (d) December and (e) Annually.

distribution of the month September and December respectively. Very high and high potential energy distribution can be found during these months because of equinox and summer solstice. Finally annual renewable energy distribution map is displayed in Figure 4(e) to know overall condition of a particular location of PNG. Potential renewable distribution is higher in its intensity diagonally from the south-west to the north-east. Very high potential renewable energy can be found in most areas of Manus, New Ireland, North Solomon, West New Britain, Northern, Central and Milne Bay; a larger portion of East New Britain; and the northern part of West and East Sepik, Central, Morobe and eastern part of Madang province.

5. Conclusion and Recommendations

Spline interpolation is preferred to kriging and inverse distance weighted because it is faster [13] and easier to use with an input of uniformly-gridded point data. For potential renewable energy distribution modeling, we used surface albedo index, earth’s skin temperature, monthly averaged clear sky insolation incident on a horizontal surface, and wind speed. There is also possibility to bring other source of renewable energy into play like geothermal, water, sea wave etc.; then, the model may predict even more accurate results. As the result suggests, two variables (clear sky insolation incident and wind speed) out of four variables are likely to be major sources of renewable energy in PNG. The potential renewable energy distribution map can help to establish sustainable energy production for the country. The incentive to use 100% renewable energy, for electricity, transport, or even total primary energy supply globally, has been motivated by global warming and other ecological as well as economic concerns. Renewable energy use has grown much faster than even advocates anticipated [20] . Energy costs with a wind, solar, water system should be similar to today’s energy costs [21] , thus helping confront issues related to climate change, energy security, and the escalation of energy costs. Renewable energy is an attractive option because renewable resources available in the Papua New Guinea, taken collectively, can supply significantly greater amounts of electricity than the total current demand. The most significant barriers to the widespread implementation of large-scale renewable energy and low carbon energy strategies are primarily political and not technological. The current social/political problems in the country may have exacerbated existing problems with regards to provision of energy infrastructure to the country’s population. According to the Post Carbon Pathways report [22] , which reviews many international studies, the key roadblocks are climate change denial, the fossil fuel lobby, political inaction, unsustainable energy consumption, outdated energy infrastructure, and financial constraints.

Cite this paper

SaileshSamanta,Sammy S.Aiau, (2015) Spatial Analysis of Renewable Energy in Papua New Guinea through Remote Sensing and GIS. International Journal of Geosciences,06,853-862. doi: 10.4236/ijg.2015.68069

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