Forecast of the Small Wind Turbines Sales with Replacement Purchases and with or without Account of Price Changes
The purpose of the paper is to estimate the US small
wind turbines market potential and forecast the small wind turbines
sales in the US. The forecasting method is based on the application of
the Bass model and the generalized Bass model of innovations
diffusion under replacement purchases. In the work an exponential
distribution is used for modeling of replacement purchases. Only one
parameter of such distribution is determined by average lifetime of
small wind turbines. The identification of the model parameters is
based on nonlinear regression analysis on the basis of the annual
sales statistics which has been published by the American Wind
Energy Association (AWEA) since 2001 up to 2012. The estimation
of the US average market potential of small wind turbines (for
adoption purchases) without account of price changes is 57080
(confidence interval from 49294 to 64866 at P = 0.95) under average
lifetime of wind turbines 15 years, and 62402 (confidence interval
from 54154 to 70648 at P = 0.95) under average lifetime of wind
turbines 20 years. In the first case the explained variance is 90,7%,
while in the second - 91,8%. The effect of the wind turbines price
changes on their sales was estimated using generalized Bass model.
This required a price forecast. To do this, the polynomial regression
function, which is based on the Berkeley Lab statistics, was used. The
estimation of the US average market potential of small wind turbines
(for adoption purchases) in that case is 42542 (confidence interval
from 32863 to 52221 at P = 0.95) under average lifetime of wind
turbines 15 years, and 47426 (confidence interval from 36092 to
58760 at P = 0.95) under average lifetime of wind turbines 20 years.
In the first case the explained variance is 95,3%, while in the second
– 95,3%.
[1] Churkin, V., Forecast of the innovative products sale (photovoltaic
systems are considered). Proceedings of the 2nd International
Conference Innovation Management and Company Sustainability,
Prague, 2014, pp.482-493 http://imacs.vse.cz/wp-content/uploads/
2014/08/IMACS-2014_Proceedings_final.pdf.
[2] Miller, A.H., Strong patent growth for renewables indicates bright future
for solar. http://www.cleanenergyauthority.com/solar-energy-news/
strong-patent-growth-for-renewables-indicates-bright-future-for-solar-
101413.
[3] Churkin, V.I., Sales forecasts of innovative products within the
macroeconomic factors (using the example of small wind turbines). St.
Petersburg State Polytechnical University Journal. Economics, 1(1),
2013, pp. 104– 112.
[4] Bass, Frank M., A new product growth model for consumer durables.
Management Science, 15(5), 1969, pp. 215–227.
[5] Mahajan, V., Mason, C.H., & Srinivasan, V., An evaluation of
estimation procedures for new product diffusion models. Innovation
Diffusion Models of New Product Acceptance, eds. V. Mahajan and Y.
Wind, (Ballinger Cambridge, Massachusetts), 1986, pp. 203-232.
[6] Srinivasan, V., & Mason C.H., Nonlinear least squares estimation of
new product diffusion model. Marketing Science, 5(2), 1986, pp. 169-
178.
[7] Schmittlein, D. C., & Mahajan V., Maximum likelihood estimation for
an innovational diffusion model of new-product acceptance.
Management Science, 1(1), 1982, pp. 57-78.
[8] Mahajan, V., & Sharma S., A simple algebraic estimation procedure for
innovation diffusion models of new product acceptance. Technological
Forecasting and Social Change, 30, 1986, pp. 331-346.
[9] Bass, F. M., Krishnan, T. V., & Jain, D. C., Why the Bass model fits
without decision variables. Marketing Science, 13, 1994, pp. 119–130.
[10] Stimmel, Ron., Status of the U.S. Small-wind market.
www.awea.org/smallwind.
[11] United States Department of Energy (DOE). 2012 Wind technologies
market report, www.osti.gov/bridge.
[12] Wiser R.H., Tracking and understanding trends in the U.S. Wind power
market: 2007 Annual report on U.S. Wind power installation, cost, and
performance trends. Lawrence Berkeley National Laboratory
DOE/NREL Wind Powering America Webinar, July 9, 2008.
http://apps2.eere.energy.gov/wind/windexchange/pdfs/wpa/2008/wiser_
2007_annual_wind_market_report_webcast.pdf.
[13] Wiser R., Bolinger M., Annual report on U.S. wind power installation,
cost, and performance trends: 2006. http://www1.eere.energy.gov/wind/
pdfs/wiser_data_report_summary_2006.pdf.
[14] Brian T. Ratchford, Siva K. Balasubramanian, Wagner A. Kamakura,
Diffusion models with replacement and multiple purchases (Chapter 6).
New-product diffusion models, ed. Vijay Mahajan, Eitan Muller, Yoram
Wind. Springer Science+Business Media, Inc. 2000, Springer
Science+Business Media Inc., New York, 2000, pp. 123-140.
[15] Olson, J. and Choi, S., A product diffusion model incorporating repeat
purchases. Technological Forecasting and Social Change, 27 (4), 1985,
pp. 385-397.
[16] Kamakura, W. A. and Balasubramanian, S. K., Long-term forecasting
with innovation diffusion models: The impact of replacement purchases.
Journal of Forecasting, 6, 1987, pp. 1-19.
[1] Churkin, V., Forecast of the innovative products sale (photovoltaic
systems are considered). Proceedings of the 2nd International
Conference Innovation Management and Company Sustainability,
Prague, 2014, pp.482-493 http://imacs.vse.cz/wp-content/uploads/
2014/08/IMACS-2014_Proceedings_final.pdf.
[2] Miller, A.H., Strong patent growth for renewables indicates bright future
for solar. http://www.cleanenergyauthority.com/solar-energy-news/
strong-patent-growth-for-renewables-indicates-bright-future-for-solar-
101413.
[3] Churkin, V.I., Sales forecasts of innovative products within the
macroeconomic factors (using the example of small wind turbines). St.
Petersburg State Polytechnical University Journal. Economics, 1(1),
2013, pp. 104– 112.
[4] Bass, Frank M., A new product growth model for consumer durables.
Management Science, 15(5), 1969, pp. 215–227.
[5] Mahajan, V., Mason, C.H., & Srinivasan, V., An evaluation of
estimation procedures for new product diffusion models. Innovation
Diffusion Models of New Product Acceptance, eds. V. Mahajan and Y.
Wind, (Ballinger Cambridge, Massachusetts), 1986, pp. 203-232.
[6] Srinivasan, V., & Mason C.H., Nonlinear least squares estimation of
new product diffusion model. Marketing Science, 5(2), 1986, pp. 169-
178.
[7] Schmittlein, D. C., & Mahajan V., Maximum likelihood estimation for
an innovational diffusion model of new-product acceptance.
Management Science, 1(1), 1982, pp. 57-78.
[8] Mahajan, V., & Sharma S., A simple algebraic estimation procedure for
innovation diffusion models of new product acceptance. Technological
Forecasting and Social Change, 30, 1986, pp. 331-346.
[9] Bass, F. M., Krishnan, T. V., & Jain, D. C., Why the Bass model fits
without decision variables. Marketing Science, 13, 1994, pp. 119–130.
[10] Stimmel, Ron., Status of the U.S. Small-wind market.
www.awea.org/smallwind.
[11] United States Department of Energy (DOE). 2012 Wind technologies
market report, www.osti.gov/bridge.
[12] Wiser R.H., Tracking and understanding trends in the U.S. Wind power
market: 2007 Annual report on U.S. Wind power installation, cost, and
performance trends. Lawrence Berkeley National Laboratory
DOE/NREL Wind Powering America Webinar, July 9, 2008.
http://apps2.eere.energy.gov/wind/windexchange/pdfs/wpa/2008/wiser_
2007_annual_wind_market_report_webcast.pdf.
[13] Wiser R., Bolinger M., Annual report on U.S. wind power installation,
cost, and performance trends: 2006. http://www1.eere.energy.gov/wind/
pdfs/wiser_data_report_summary_2006.pdf.
[14] Brian T. Ratchford, Siva K. Balasubramanian, Wagner A. Kamakura,
Diffusion models with replacement and multiple purchases (Chapter 6).
New-product diffusion models, ed. Vijay Mahajan, Eitan Muller, Yoram
Wind. Springer Science+Business Media, Inc. 2000, Springer
Science+Business Media Inc., New York, 2000, pp. 123-140.
[15] Olson, J. and Choi, S., A product diffusion model incorporating repeat
purchases. Technological Forecasting and Social Change, 27 (4), 1985,
pp. 385-397.
[16] Kamakura, W. A. and Balasubramanian, S. K., Long-term forecasting
with innovation diffusion models: The impact of replacement purchases.
Journal of Forecasting, 6, 1987, pp. 1-19.
@article{"International Journal of Business, Human and Social Sciences:70089", author = "V. Churkin and M. Lopatin", title = "Forecast of the Small Wind Turbines Sales with Replacement Purchases and with or without Account of Price Changes", abstract = "The purpose of the paper is to estimate the US small
wind turbines market potential and forecast the small wind turbines
sales in the US. The forecasting method is based on the application of
the Bass model and the generalized Bass model of innovations
diffusion under replacement purchases. In the work an exponential
distribution is used for modeling of replacement purchases. Only one
parameter of such distribution is determined by average lifetime of
small wind turbines. The identification of the model parameters is
based on nonlinear regression analysis on the basis of the annual
sales statistics which has been published by the American Wind
Energy Association (AWEA) since 2001 up to 2012. The estimation
of the US average market potential of small wind turbines (for
adoption purchases) without account of price changes is 57080
(confidence interval from 49294 to 64866 at P = 0.95) under average
lifetime of wind turbines 15 years, and 62402 (confidence interval
from 54154 to 70648 at P = 0.95) under average lifetime of wind
turbines 20 years. In the first case the explained variance is 90,7%,
while in the second - 91,8%. The effect of the wind turbines price
changes on their sales was estimated using generalized Bass model.
This required a price forecast. To do this, the polynomial regression
function, which is based on the Berkeley Lab statistics, was used. The
estimation of the US average market potential of small wind turbines
(for adoption purchases) in that case is 42542 (confidence interval
from 32863 to 52221 at P = 0.95) under average lifetime of wind
turbines 15 years, and 47426 (confidence interval from 36092 to
58760 at P = 0.95) under average lifetime of wind turbines 20 years.
In the first case the explained variance is 95,3%, while in the second
– 95,3%.", keywords = "Bass model, generalized Bass model, replacement
purchases, sales forecasting of innovations, statistics of sales of small
wind turbines in the United States.", volume = "9", number = "6", pages = "1877-6", }