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In order to compliment the growing use of renewable energies in the US, additional technologies must be employed on the bulk power system. This paper aims to forecast the most probable energy storage technologies.

The methodology was deployed in two steps: evaluate the potential energy storage technologies that could complement a wind turbine or photovoltaic system; and forecast which of these technologies is best poised to become a viable solution to the energy storage problem facing these renewable technologies.

Based on the publication and patent data, compressed air energy is set to be the fastest growing complimentary technology to wind energy. Two of these types of plants are currently in existence today as mentioned previously indicating the technology is commercially available. This technology has great potential; however, implementing this technology involves finding or creating underground airtight caverns in usable locations.

The number of variables have been limited due to the methodologies chosen for this analysis. The research can be expanded using other criteria such as cost, cost of capital, economies of scale, environmental concerns, social and political constraints.

This paper provides an assessment that was indicated as necessary by those who identified the need for the development of energy storage technologies for future electricity generation.

The electric utility industry, unlike most other technology‐intensive industries, does not spend much money or effort on research and development. Many utilities do not possess an in‐house R&D facility, nor is there an R&D line item in their budgets. Over the last several decades the rate of change in the electric utility industry has been very slow and in‐house R&D efforts have not been required. As the rate of change in the industry is beginning to change, the need to pursue R&D is increasing. The electric utility industry is responding to this increasing requirement by increasing R&D budgets, and in some cases re‐initiating the R&D process within individual utilities. The purpose of this paper is to focus on R&D portfolio management efforts of various actors from different industrial sectors, to find out the best practices by using benchmarking method.

The paper used case study approach and on‐site interviews as research methods.

The authors found that R&D management is in its infancy in the electric utility sector, while the methods established in the manufacturing sector are applicable there.

This study is exploring R&D management in the electric utility sector and contributes to the service innovation research stream.

The purpose of this paper is to use bibliometric analysis to forecast RFID technology and uses the adoption of barcode scanner to model the RFID scanner adoption in the US retail market.

Forecasting emerging technologies and identifying the rate of diffusion of products based on these technologies is difficult because of lack of data. This paper uses techniques such as bibliometric analysis and Bass model based on analogous products.

The authors were able to come up with a good Bass model for the adoption of RFID scanners in the retail sector. And according to the Bass model it appears that it will take at least seven or eight years for the retailers to adopt to this new technology at their point‐of‐sale.

This paper attempts to use bibliometric analysis and Bass model for forecasting technologies and provides a new research discussion as results from these two are compared.

This paper aims to forecast the future of data storage technologies, using the case of two major technologies driving the data storage world; hard disk drive (HDD) or conventional longitudinal recording and flash memory.

Four principal tools of forecasting technology are applied to present the coming future of data storage technologies. These tools consist of bibliometric trend analysis, patent trend analysis, technology cycle time (TCT), and growth curve. Numbers of publications each year and cumulative patents are used to analyze the future of these competitive technologies. The median age of the patents is applied to find the technology lifecycle of both technologies. Finally, areal density of HDD is plotted on the growth curve to forecast the saturation point of researching on conventional longitudinal recording.

The results produced from utilizing these tools indicate that these two technologies are continuously under development. It is found that the growth of improving areal density of conventional recording will be nearing zero by year 2013 and the maximum growth rate of development was in year 2006.

The forecasting is based on principal technologies of conventional longitudinal recording of HDD. Also, NOR and NAND are considered as two main technologies dominating flash memory. Other emerging data storage technologies such as holographic data storage, phase‐change memory (PCM) are not included in this paper. Likewise, the growth curve of flash memory is not analyzed as part of this paper.

This paper forecasts the future of data storage technologies. Integration of scientific indicators and growth curves is demonstrating a powerful tool for forecasting technology futures.

The purpose of this study is to investigate whether news media as a form of social communication regarding pressing, important, and contemporary issues could be used as a leading indicator of technology adoption. For technology foresight, monitoring and analyzing energy technologies is in the spotlight due to their strategic importance to the agenda of competitive and sustainable economic growth.

The trends in renewable energy production and news media are determined and compared to draw inferences in foresight concerning the use of renewable energy technologies in energy production. Consequently, the data concern wind and solar energy production and their representation in news media in Germany and the UK. The analysis proceeds as a quasi-experiment treating each yearly observation of energy production as a function of news media data with lagged variables.

The study reveals consistent dynamics in the lag in the primary production of energy in related technologies compared to the media visibility of the respective technologies.

This study explores the prospects of using news media data in foresight analysis concerning renewable energy production and provides many fruitful avenues for foresight research in investigating relationships between technology adoption and media exposure.

In this paper, an analysis is presented of the research funding towards nanotechnology at the National Nanotechnology Initiative (NNI) and its relationship to the research output in Nanoscope, an application area of nanotechnology.

The paper analyzes the data collected from 1997 till 2006 and derives a definitive time lag between the allocation of research funds and issued patents and published journals. This assessment is achieved by identifying growth trends in patents, funds and publications and doing a curve‐fit analysis using the Fisher‐Pry model. Linear regression analysis is used to show the correlation between the funding and research outputs. Alongside, non‐linear programming objective function optimization technique is used to derive the time lag in years for each of the research outputs from the year of funds granted.

This paper demonstrated that there is a strong correlation between research funding and different research outputs. The time lag between funding and patents issued is evident from the patent trend analysis and Bibliometric analysis. In the case of Nanoscope, the patent time lag was found to be approximately five to six years, for journal article it was approximately two to three years and conference presentations happened right after the funding. The research outputs showed similar trends and were found to be interdependent as evident from our mathematical analysis.

While this study has shown that lag times exist within the chosen example of Nanoscope, and furthermore can be calculated to a precise degree, further data points in terms of additional emerging technologies would support the hypothesis in a more general term. A future study can look at developing technology roadmaps of the future based on the funding happening today.

The work takes bibliometric analysis to a further intelligence and establishes key linkages between these indicators.

The purpose of this paper is to explore a very new approach of creating scenarios with fuzzy cognitive maps (FCM); specifically to create scenarios for wind energy deployment.

In this paper a small exercise is conducted to illustrate the usefulness of FCM‐based scenarios.

Scenario‐based planning is often used in technology management and scenarios are generated to cater for uncertainty and facilitate development of responsive and robust strategic plans. Scenario analysis helps to identify different alternatives of the future state of technologies, needs, policies and environment. Scenario planning helps to overcome thinking limitations by presenting multiple futures. FCMs are based on causal cognitive maps and combine the benefits of both qualitative and quantitative analysis. Domain knowledge of multiple experts can be integrated into an FCM to present collective knowledge base.

This is a conceptual paper exploring use of fuzzy cognitive maps to create scenarios for wind energy deployment. The paper provides scenarios and a way to integrate them into technology roadmaps.

The objective of this study is to provide a support tool for managers to identify future business opportunities.

The proposed model consists of eight-steps. For the first step the authors propose two studies: trends analysis and change drivers' identification; these help to identify the most relevant trends in areas that may affect the business activities. All this information is put into a Delphi process with the help of experts in order to find the common denominator of where the future is going; afterwards, with this information, the authors can build events and scenarios for the business opportunities. Then, a diagnostic of the study area, sector or organization through the dynamic diagnostic methods is performed.

Once the opportunities have been identified, a strategic analysis that allows decision making to prioritize and define the activities or projects that may be achievable in the future is done, and the result of the process is a portfolio of business opportunities projects fully defined and ready to be planned with a roadmap.

The proposed model allows the managers to consistently organize their development processes targeted to real business opportunities.