Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions

Abstract

The prediction of potential solar energy at candidate sites plays a key role in the search for high solar energy regions to accommodate solar photovoltaic facilities. However, the estimation of solar irradiation can be inaccurate when site conditions are not similar to the ones at observation stations. In particular, regional effects caused by adjacent terrains have rarely been modeled in the estimation process. This study thus presents a digital elevation map-based convolutional neural network modeling method for the prediction of annual solar irradiation under clear-sky conditions. Using map data as an input, the sole impact of neighboring topography on available solar energy can be understood at a large scale. Specifically, with elevation maps and corresponding solar irradiation maps, the mean of solar irradiation values for each map is calculated. This mean value is then used as an output to identify the non-linear and hidden relationship between such values and the input maps in training processes. The proposed network model uses terrain map datasets and hence enables the recognition and learning of complex topographic features and patterns in the datasets. As a result, the network model has a mean absolute percent error of 0.470% for testing datasets. This implies that the topographic patterns on a large scale may coincide with variations of available solar irradiation prior to the effects of weather. Thus, the integration of raster map data with solar energy prediction has emerged to systematically learn the terrain patterns in order to predict the annual amount of solar irradiation in a region of interest. This approach can aid the determination of suitable locations for the installation of solar panels.