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Low carbon hydrogen can be an excellent source of clean energy, which can combat global climate change and poor air quality. Hydrogen based economy can be a great opportunity for a country like Qatar to decarbonize its multiple sectors including transportation, shipping, global energy markets, and industrial sectors. However, there are still some barriers to the realization of a hydrogen-based economy, which includes large scale hydrogen production cost, infrastructure investments, bulk storage, transport & distribution, safety consideration, and matching supply-demand uncertainties. This paper highlights how the aforementioned challenges can be handled strategically through a multi-sector industrial-urban symbiosis for the hydrogen supply chain implementation. Such symbiosis can enhance the mutual relationship between diverse industries and urban planning by exploring varied scopes of multi-purpose hydrogen usage (i.e., clean energy source as a safer carrier, industrial feedstock and intermittent products, vehicle and shipping fuel, and international energy trading, etc.) both in local and international markets. It enables individual entities and businesses to participate in the physical exchange of materials, by-products, energy, and water, with strategic advantages for all participants. Besides, waste/by-product exchanges, several different kinds of synergies are also possible, such as the sharing of resources and shared facilities. The diversified economic base, regional proximity and the facilitation of rules, strategies and policies may be the key drivers that support the creation of a multi-sector hydrogen supply chain in Qatar.

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

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