Statistical considerations in the fitting of growth curves

Willis, Raymond E.

doi:10.1016/0040-1625(79)90072-6

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Pearl curve (Pearl and Reed, 1920) is also a logistic symmetrical curve that was first used in forecasting population growth (Winsor, 1932;Wikipedia, 2011;Tsoularis and Wallace, 2002;Spurr and Arnold, 1948;Willis, 1979) describe fitting of the Pearl curve while (Young, 1993) summarizes and compares technological growth curve models.…”

Section: Methodsmentioning

confidence: 99%

Forecasting renewable energy production in the US

2012

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Purpose -This paper aims to present a forecast for renewable energy production in the USA. Growth curves are used to conduct the forecasts.Design/methodology/approach -The analysis is based upon a literature review, supplemented by collection of secondary data. The study then focuses on applying the Pearl growth curve.Findings -The authors' results show that biomass energy production is growing the fastest followed by geothermal and wind. Additionally, the forecast for solar energy production shows little to no growth over the next two decades.Research limitations/implications -If the US government hopes to achieve its goals in renewable energy, considerable funding and incentives will have to be put forth to accelerate the growth of renewable energy. Since the biomass technology is already growing nicely it makes sense to put the additional resources behind the other three technologies to close the 10.3 percent gap being forecasted. The government also needs to put more funding into dual renewable plants such as wind or solar combines with pumped hydro, this will ensure environmental and reliability are both maintained. Finally, for renewable energies to be competitive in the long term, considerable research needs to go into driving down the cost so there is not a need for subsidies.Originality/value -This study provides value in providing a forecast for expected future growth for renewable energy sources.

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Section: Methodsmentioning

confidence: 99%

Forecasting renewable energy production in the US

2012

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“…Although there are numerous techniques available for technology forecasting, the selection of specific forecasting methods used within this paper was driven by the availability of data that could be used to predict the subset of energy storage technologies (Willis, 1979). Willis (1979) suggested that forecasts should be developed as a baseline to study the behaviors of the specific trend being studied. Daim et al (2006) stated that direct measurement of a given technology may not be possible, and that a combination of tools could be necessary to properly forecast adoption rates.…”

Section: Methodsmentioning

confidence: 99%

“…The Pearl formula can be seen below in equation ( 2). Willis indicates that the growth curve originally suggested by Pearl and Reed may introduce problems with ''statistical evaluation of model fit and in estimating the precision of forecasts'', developed with this method (Willis, 1979). Willis further explains how using a curve fitting approach (suggested by Yule) a more precise method is obtained:…”

Section: Methodsmentioning

confidence: 99%

Forecasting energy storage technologies

Harell

Daim

2009

Foresight

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Purpose -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.Design/methodology/approach -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.Findings -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.Research limitations/implications -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.Originality/value -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.

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The impact on the performance of growth curve models of changes in parameter re‐estimation period and other factors

Price

Sharp

1988

Journal of Forecasting

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A simulation model of a real electricity supply undertaking was used to provide a financial performance measure for growth curve forecasting models. The impact on financial performance was determined when changes were made in (1) the method of estimating the model parameters, (2) the period between re-estimations, (3) the growth curve fitted and (4) the amount of smoothing of the demand time-series. The response to variation of the parameter review period was found to behave surprisingly, in that it exhibited different signs for two different estimation methods. Changes in re-estimation period explained somewhat more of the variation in performance than did a change in growth curve. Correcting the demand series for conditions which were known to be abnormal improved performance. KEY WORDS Growth curves Simulation Forecasting performance LogisticSurveys conducted by Balachandra (1980), Fildes (1978 and Naylor and Schauland (1976) confirm the popularity of fitting growth curves as a forecasting method. Meade (1984) has reviewed a group of applications in which growth curves were used to forecast demand across a broad range of industrial sectors. Among the areas to which this approach has been applied is that of forecasting the demand for electricity (Bodger and Tay, 1986; Bossert, 1977;Stanton and Gupta, 1970). In a previous study, Price and Sharp (1985) examined the impact on the financial performance of a large electrical supply undertaking of varying the method of forecasting peak demand used in the capacity acquisition process. A group of four methods were examined: autoregressive, Box-Jenkins, Holt and fitting a Logistic curve. When the interval between reestimation of the forecasting models was changed from annually to five-yearly it was observed that the financial performance improved for the first three methods but deteriorated for the logistic fitting. Subsequent additional analyses of variance of these results showed that for each of the four methods considered individually, the update or frequency of re-estimation main effect was significant at the 0.05 level.

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Statistical considerations in the fitting of growth curves

Cited by 5 publications

References 7 publications

Forecasting renewable energy production in the US

Forecasting renewable energy production in the US

Forecasting energy storage technologies

The impact on the performance of growth curve models of changes in parameter re‐estimation period and other factors

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