Unemployment is an essential problem for developing countries, which has a direct and major role in economy of a country. Understanding the pattrens of unemployment rate is critical now a days and has drawn attention of researcher from all fields of study across the globe. As unemployment plays an important role in the planning of a country’s monetary progress for policymakers and researcher. Determining the unemployment rate efficiently required an advance modeling approach. Recently,numerous studies have relied on traditional testing methods to estimate the unemployment rate. Unemployment is usually nonstationary in nature. As a result, demonstrating them using traditional methods will lead to unpredictable results. It needs a hybrid approach to deal with the prediction of unemployment rate in order to deal with the issue associated with traditional techniques. This research primary goal is to examine the effect of the Covid-19 pandemic on the unemployment rate in selected countries of Asia through advanced hybrid modeling approach, using unemployment data of seven developing countries of Asian: Iran, Sri Lanka; Bangladesh; Pakistan; Indonesia; China; and India,and compare the results with conventional modeling approaches. Finding shows that the hybrid ARIMA-ARNN model outperformed over its competitors for Asia developing economies. In addition, the best fitted model was utilised to predict five years ahead unemployment rate. According to the findings, unemployment will rise significantly in developing economies in the next years, and this will have a particularly severe impact on the region’s economies that aren’t yet developed.

This research investigates the optimal management of electric and heat energies in a hybrid energy system (HES). In the studied HES, a pair of photovoltaic and battery storage devices is used to supply the electricity demand, and a boiler system to supply the heat demand directly. In addition, a modified cycle power plant acted as a combined heat and power (CHP) unit to increase the generation capacity and supply reliability. The HES is also able to connect to the electric grid to exchange power according to real-time energy prices. The uncertainty of renewable generation, demand levels, and energy prices challenge the decision-making process. To deal with the uncertainty of these overlapping parameters, a comprehensive information-gap decision theory (IGDT) approach is proposed in this paper that, despite other works, considers the uncertainties in an integrated framework and derives risk-averse and risk seeker strategies in different steps. The problem is modeled as mixed-integer linear programming and solved using the GAMS optimization package. Concerning simulation results, from the viewpoint of a risk-seeking decision maker, the increment of the uncertainty degree by 10.906% results in a reduced operating cost of 8.6%. From the viewpoint of a risk-averse decision maker, the increment of the uncertainty degree by 10.208% results in 8.6% more operating cost.