Probabilistic temperature change projections and energy system implications of greenhouse gas emission scenarios

<p>The co-ordination of non-conventional energy technologies such as solar, wind, geothermal, biomass and ocean are gaining significance in India due to more energy requirements and high greenhouse gas emission. In this assessment, the sustainability of emerging the gird isolated hybrid solar photovoltaic (PV)/wind turbine (WT)/diesel generator (DG)/battery system for Arunai Engineering College (India) building is evaluated. The techno- economic and environmental research was inspected by HOMER Pro software by choosing the optimal combination depends on size of the components, renewable fraction, net present cost (NPC), cost of energy (COE) and greenhouse gas (GHG) emission of the hybrid system. From the acquired outcomes and sensitivity investigation, the optimal PV-WT-DG- Battery combination has a NPC of $28.944.800 and COE $0.1266/kWh, with an operating cost of $256.761/year. The grid isolated hybrid system is environmentally pleasant with a greenhouse gas emission of 2.692 kg/year with renewable fraction of 99.9%.</p>

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Future Predictions of Rainfall and Temperature Using GCM and ANN for Arid Regions: A Case Study for the Qassim Region, Saudi Arabia

Water ◽

10.3390/w10091260 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1260 ◽

Cited By ~ 12

Author(s):

Khalid Alotaibi ◽

Abdul Ghumman ◽

Husnain Haider ◽

Yousry Ghazaw ◽

Md. Shafiquzzaman

Keyword(s):

Saudi Arabia ◽

Greenhouse Gas ◽

Greenhouse Gas Emission ◽

Data Driven ◽

Emission Scenarios ◽

Gas Emission ◽

The Future ◽

Emissions Scenarios ◽

The Impact

Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. Furthermore, their results are associated with uncertainties and variations for different GCMs for various greenhouse gas emission scenarios. Data-driven models including artificial neural networks (ANNs) and adaptive neuro fuzzy inference systems (ANFISs) can be used to predict long-term future changes in rainfall and temperature, which is a challenging task and has limitations including the impact of greenhouse gas emission scenarios. Therefore, in this research, results from various GCMs and data-driven models were investigated to study the changes in temperature and rainfall of the Qassim region in Saudi Arabia. Thirty years of monthly climatic data were used for trend analysis using Mann–Kendall test and simulating the changes in temperature and rainfall using three GCMs (namely, HADCM3, INCM3, and MPEH5) for the A1B, A2, and B1 emissions scenarios as well as two data-driven models (ANN: feed-forward-multilayer, perceptron and ANFIS) without the impact of any emissions scenario. The results of the GCM were downscaled for the Qassim region using the Long Ashton Research Station’s Weather Generator 5.5. The coefficient of determination (R2) and Akaike’s information criterion (AIC) were used to compare the performance of the models. Results showed that the ANNs could outperform the ANFIS for predicting long-term future temperature and rainfall with acceptable accuracy. All nine GCM predictions (three models with three emissions scenarios) differed significantly from one another. Overall, the future predictions showed that the temperatures of the Qassim region will increase with a specified pattern from 2011 to 2099, whereas the changes in rainfall will differ over various spans of the future.

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