• Energy Research

In today's competitive dynamic world/markets, providing a desirable framework for exploring future perspectives is a crucial challenge to support robust decision making and proper policy making process. This research proposes a novel framework that develops plausible future energy scenarios through the Fuzzy Cognitive Map (FCM) technique. As a new method in scenario planning, FCM attempts to present a set of rational, reliable and credible scenarios together with analyzing dynamic behaviors of parameters. The integrated approach encompasses STEEP analysis to identify parameters, Cross Impact Analysis (CIA) to determine key drivers, Morphological analysis for scenario selection, and FCM to develop semi-quantitative scenarios. The new proposed scenario development approach brings the benefits of both quantitative and qualitative analysis together, which is not limited to the investigation of few pre-defined scenario drivers. As a research case, the proposed methodology was examined to detect plausible trends for Iran's oil production in the post-sanction era. The implemented FCM simulations indicated that in three scenarios oil production rises, as growth would be significant for the first two. The fourth projection is the most pessimistic future that can be imagined in the post-sanction era where the country faces massive investment backlogs.

Decision-makers are concerned with the inherent complexity of the modern world’s markets. However, price fluctuations, environmental concerns, technological development, emerging markets, political challenges, and social expectations made the 21st century more dynamic and complex. From a policy-making perspective, it is vital to uncover future trends. This article proposed that artificial intelligence (AI) can improve interpretations in complex markets, such as financial and energy markets. In a complex environment, it is critical to investigate maximum available input features to ensure no valuable informative feature is neglected. Some AI-based models are investigated and presented that AI-based models can successfully uncover future trends. From a scenario development perspective, purified input features subset refers to driving forces which shape alternative futures. Results showed that using AI can improve our understanding of how input features influence future behaviors and simultaneously improve prediction accuracy and reliability.

The successful deployment of the solar water heater (SWH) in the residential sector relies on the household’s bounded rational decision-makers to accept this system. The decision is shaped by a wide spectrum of predictors that form heterogeneous behaviour. Over the past years, research has employed a wide range of these predictors to understand their role in the decision and predict the behaviour and diffusion rate of SWHs. This review primarily identifies economic and technical predictors of 100 quantitative and qualitative studies on the residential SWH adoption decision. For the identified predictors, their characteristics and popularity are explored in a structured and coherent framework. The review further investigates the correlation between the identified predictors and the adoption decision from 97 of the 100 initially reviewed studies. The outcome of the research revealed 123 (56 economic and 67 technical) predictors that were classified into seven categories. ‘Financial incentives’ and ‘perceived attitude towards government policies’ are among the most popular economic predictors, whereas ‘house type’ and ‘knowledge of SWH’ were the most frequent technical factors in the research. Analysing the correlation between 99 predictors and the decision also unfolded that certain attitudinal attributes have a stronger influence on the residential SWH take-up than some common factors (e.g., electricity cost, technical variables).

Recently, the natural gas (NG) global market attracted much attention as it is cleaner than oil and, simultaneously in most regions, is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and pre-processing selected input vector. Then a hybrid artificial neural network (ANN) which is equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g., Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) were selected as the most relevant feature via the DM step. Then, the hybrid learning prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different five error based evaluation statistics consist of R2, MAE, MAPE, MBE, and RMSE. In addition, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process. The comparison showed that DmGNn overcame dramatically a simple GNn. Also, a looser prediction model, such as a generalized neural network with purified input features obtained a larger R2 indicator (=0.9864) than the GNn (=0.9679).

Abstract Widespread use of promising solar energy is an integral attribute of governments toward global efforts in mitigating future emission and reducing the fossil fuel consumption trajectory. However, the outlook of solar energy development at the strategic level is driven by inherent uncertainties in a long-term perspective. As these unpredictable volatilities come into play, they heavily influence the future trends formed by a range of exogenous and endogenous factors including Political, Economic, Social, Technological, Environmental, and Legal (PESTEL). This study aimed at identifying, characterizing, and analyzing influential PESTEL factors shaping the dynamics of Solar Photovoltaics (SPV) in an uncertain environment. For this purpose, the Fuzzy Cognitive Mapping (FCM) method was employed for the case of Iran to investigate how involved factors dynamically interact and collectively behave in system. A participatory stepwise framework was developed consisting of three steps by conducting a survey and two workshops with the participation of multidisciplinary stakeholders. Aa a result, a semi-quantitative model was formed in an integrated FCM comprising 31 interwoven concepts. To further outline possible future trends, five key concepts were determined that explored four plausible projections of Iran's SPV sector. The scenario outcome suggests that the industry is profoundly affected by external economic and political factors. Two scenarios “Cornered in a Dead-End” and “At Wits' End” project that, by 2030, the sector experiences a declining growth trend. “The Period of Tolerance” scenario does not envisage a dramatic change from the current trajectory, while in only “Blue Sky” the country's SPV sector anticipate a moderate growth.

Purpose Many devices needed electrical power to work, thus, major energy carriers such as oil and gas were used to generate electrical power via converter mechanisms and special technologies. The microturbine is a developed technology that is remarkable for its relatively high performance and ability to use several types of fuels. Microturbines are economically feasible because of the production of combined heat and power and small-scale applicability. This study aims to investigate microturbine technology development to support modern energy access in a developing country (i.e. Iran). Design/methodology/approach This paper presents a technology foresight methodology to create plausible futures of microturbine development in Iran when the country faces different driving forces and uncertainties. On other hand, the paper deals with a theoretical question: how to select appropriate foresight methodology? A procedure is proposed, which equipped the research team to select appropriate method combinations based on Popper’s diamond. Finally, the selected methodology includes defining focal issues environmental scanning and patent analysis aimed at developing five plausible scenarios for microturbine development future in Iran and creating shared visions among policymakers. Findings This paper proposed a series of scenarios on the path to developing microturbine technology. The scenario development logic in a participatory way contains a common four-quarter technique that attempts to depict scenarios based on two critical uncertainties inclusive energy price and technology obsolescence, which will shape the future. Also, a scenario is presented to describe a wild card that can disturb the desired futures. Such materials help decision-makers to policies under plausible conditions that guarantee a robust policy basket. Originality/value The originality of this paper can be studied based on two aspects, first, the methodology that provides a systematic method selections procedure in an emerging complex technology development program. Second, from the practical aspect, this paper is one of the very first attempts to manage the microturbine technology development program. Then, results are used to feed the policy-making process in Iran.

Purpose Studying previous science and technology (S&T) foresight activities reveals information that helps decision makers to redesign policy-making templates aimed at dealing with new millennium challenges. To propose policy recommendations about further S&T foresight programs in Iran, this paper aims to propose a three-phase process to study historical S&T foresight activities at national and sub-national level since 2005 to 2015, to analyze the state of selected activities to discover weaknesses and potential solutions and, finally, to provide strategies and tactics to improve further S&T foresight activities through an expert-based process. Design/methodology/approach This paper provides a three-stage methodology, designed to survey Iranian historical foresight practices (study) using scoping framework equipped with additional features, diagnosis and evaluating (analyze) and finally proposing recommendations to organize and implement more efficient further foresight practices (design) to initialize further practices in developing countries such as Iran. Findings Although concerns about future and the importance of foresight activities are raised however Iranian foresight community needs to be developed. As noted in Section 5, Iranian foresight facilitators and specialist are biased to limited methodologies and methods; therefore, creating foresight networks and developing communities is strongly recommended. Research limitations/implications The main constraint of this research was lack of valid data in the case of some Iranian S&T foresight programs. Originality/value Iran as a developing country needs to plan for long-run programs; however, there is no integrated study which reviews and analyzes the previous attempts to dedicate insights about how to reframe existing foresight paradigms. As foresight practices facilitate the paths toward sustainability, analyzing and diagnosis of a series of foresight practices in a devolving country may initialize designing such efforts in less developed world.

Abstract For decades, energy has prevailed as a critical policymaking concern at national and international levels. Today, energy systems, the global markets and their trends are more complex, and it is crucial for any nation or organization which seeks to grow its share in the energy markets to develop insights about potential future trends and changes. Although Iran has one the largest natural gas reserves in the world, it currently contributes little to international market supply and recently has targeted the enhancement of its role in the market. To achieve this, it must carefully consider the complexity of existing global energy markets and how they are likely to evolve in the future. Here, we develop and discuss a novel scenario synthesizing model to address the inherent uncertainty of the energy future. The model starts with a structured environmental analysis step to establish the meaningful driving forces and other influences on the natural gas global markets. The influences identified are then categorized under four classes: critical uncertainties, driving forces, descriptive, and neutral (which are removed from the study). Applying a simulation-based method, a layered scenario development model is constructed to develop plausible scenarios for two feature classes: critical uncertainties and driving forces. The developed scenarios are then combined to generate possible scenario streams. A third layer simulation is applied to generate final plausible scenarios. As a final step, scenarios are clustered to define relatively independent scenario streams, and each is discussed using descriptive features.