Geospatial AI-based approach to assess the spatiotemporal suitability of onshore wind-solar farms in Iraq

Nowadays, Renewable Energy (RE), such as wind and solar, play a vital role in meeting the increasing demand for electricity and ensuring a low-carbon future. Unfortunately, the efficiency of wind and solar plants depends on the spatially and temporally fluctuating nature of their renewable resources. These fluctuations have prompted planners and decision-makers to shift toward hybridizing such energies in an effort to make electricity generation more stable. Wind-solar hybrid plants have posed new challenges in evaluating suitable sites that meet the requirements of both types of energy. As a result, site selection models for hybrid power systems (wind-solar) have received wide attention in recent years as being a critical planning problem that needs accurate decision-making. In the existing literature, the site suitability assessment of dual-energy systems (windsolar) is frequently addressed as a Spatial Decision-Making (SDM) problem involving numerous climatic, economic, and environmental criteria. In most cases, the input factors are considered constant. However, most of these factors, such as wind speed and solar radiation, change over time. Besides, criteria are often subjectively or objectively weighted, generating biased and non-generalizable solutions. To overcome these challenges, the current research aims to develop a SpatioTemporal Decision-Making (STDM) model based on Geospatial Artificial Intelligence (GeoAI) to locate onshore wind-solar hybrid plants. The presented model seeks to fill in the existing gaps by considering the dynamic nature of decision criteria and formulating novel, more reliable global weights. To achieve the research goal, a four-stage methodology was drawn. A system of spatial evaluation criteria was first designed based on literature statistics and expert judgments supported by content validity analysis. Second, eXplainable Artificial Intelligence (XAI) was introduced to formulate novel global weights for those criteria. In this context, global geospatial data for 13 conditioning factors were collected, and 55,619 inventory samples of wind and solar stations worldwide were prepared to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). SHapley Additive exPlanations (SHAP) algorithm was applied to interpret the results of the superior model and elicit the criteria weights. In a separate third step, a new temporal criterion was developed based on temporal complementarity assessment between wind and solar resources. Pearson’s correlation coefficients were utilized to explore the synergistic patterns between the time-series dataset of wind speed and solar radiation across Iraqi territory. Finally, spatial, temporal, and exclusion criteria were incorporated along with AI weights into a unified STDM model using multiple overlay analyses in a GIS environment. The outcomes of the criteria design demonstrated that 13 factors, with an excellent Kappa value of 0.76, would form valuable content for evaluating the spatial suitability of wind and solar farms. The results indicated the superiority of the RF algorithm in both wind and solar modeling, with an overall accuracy of 90% and 89%, a kappa coefficient of 0.79 and 0.78, and an area under the curve of 0.96 and 0.95, respectively. The XAIbased importance analysis revealed higher weights for technical and economic criteria than for other environmental and social criteria. Top weights were given to the criteria of wind speed and proximity to cities (0.373 and 0.149, respectively) in locating wind farms, and the criteria of proximity to cities and air temperature (0.180 and 0.149, respectively) in locating solar farms. The southwestern regions and some eastern parts of Iraq exhibited significant temporal synergistic patterns spanning more than 6 months of the year that influence spatial decision-making. Our spatiotemporal model identified three hotspots over Iraq—South Dhi-Qar, West Diyala, and East Wasit—with a total area of 3,632 km2. The hotspots revealed exceptional suitability scores exceeding 0.8, meeting both the spatial and temporal constraints. The reported spots have the technical potential to generate electricity from wind turbines and solar PV cells at rates of 11.88– 12.58 MW and 81.31–89.51 MW, respectively. Overall, this research has led to the development of a new GeoAI-based STDM model that can find the best places to put wind-solar systems with great accuracy and consistency. Analyses of transferability, sensitivity, and uncertainty show that the GeoAI-based STDM model is more reliable than one-dimensional solutions. The advantage of the proposed model is not only to identify the technically, economically, and environmentally appropriate sites but also to ensure that they boast temporal synergistic patterns between renewables for stable power supplies around the clock. Consequently, the need to rely on energy storage systems will decrease, leading to reduced investment costs.

Text 115915.pdf Download (1MB) Official URL or Download Paper: http://ethesis.upm.edu.my/id/eprint/18255

Actions (login required)

View Item