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Technology Forecasting Essay

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Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.

Published: Mon, 27 Feb 2017

The various mathematical models being used to carry out forecasts sometimes lead to significant errors. This occurs because the development of new relationships is not taken into account and possible trends changes are considered negligible.

A major failure in forecasting arises from the fact that social and political issues are not taken into consideration when forecasting is carried out. It is impossible to predict the future based only on past data. The models that do that are actually excluding qualitative parameters such as the opinion of the individuals.

Therefore it is essential to use a different method for dealing with these problems. According to the theory of Godet, the future is not a continuation of the past but an “outcome of the wishes of various actors and the constraints imposed on them by the environment” (Godet 1982).

The configuration of prices within an energy market depends a lot on the balance between the supply and demand for energy. In order to evaluate future energy price scenarios, it is necessary to mention the parameters that affect the supply-demand balance. In 1, some typical parameters are presented.

Economic growth refers only to the quantity of goods and services produced. Energy is actually the driving force that moves every economic activity. The economy can be set in motion by specific activities that convert energy from naturally sources (e.g. solar, heat, wind, running water, fossil fuels and chemicals) into forms that will be used to produce goods and services. Eventually, a high economic growth rate implies urgent needs concerning the uninterrupted supply of energy. In response to that, the infrastructure network may choose to readjust the price of energy in order to cover the demand for stable power (Alam 2006).

According to Shafiee and Topal (2008), high prices might indicate the threat of a physical scarcity of fossil fuels (e.g. oil, gas).

If production cost increases, then producers will try to pass this cost to customers. Eventually, the price of energy will rise.

When the population increases steadily and in combination with the desire for better living conditions, the demand for energy will increase too. This leads to the requirement for additional energy production which eventually will increase prices.

When an energy market is characterized by the domination of one or at least two or three suppliers, then these companies may take advantage of the surge in demand and increase prices in order to make profits and cover financial losses.

Environmental taxation is a tool that can be used to meet environmental and national objectives. In the case of energy production, the pollutant will try to pass this additional cost to the customer. Eventually, the inability of the producer to comply with specific rules concerning the environment, will force the consumers to pay more for the same amount of energy.

2)

Among the elements that can influence the procedure of energy price planning, there are some parameters that can be quantified, such as population, economic growth, energy consumption, type of market and greenhouse gas emissions.

According to 2, three possible scenarios concerning the evolution of the earth’s population are presented. By taking as a typical reference point the year 2050, the difference between the final results of the three scenarios is rather large. The current population of earth according to U.S. Census Bureau (2009) is 6,792,134,536 billions. This number compared with the three different results reveals three different change rates.

Table 1: (Source: United Nations 2004).

Current Population

Scenario High

Scenario Medium

Scenario Low

6.79 billions

10.6 billions

8.9 billions

7.4 billions

Change concerning

current situation

+ 56.11%

+ 31.07%

+8.98%

Taking into consideration the current population of earth and the three scenarios, someone may conclude that the low scenario is impossible to occur in the future. If something unexpected takes place (e.g. fast spread of a certain virus, war, births control etc.) slight changes may occur and the three scenarios might arise in a different form.

Another parameter that can affect the future scenarios of energy pricing is the magnitude of economic growth. Economic growth is often measured through the measurement of gross domestic product (GDP).According to 3, the average annual increase for the period 1981-2008, compared to the GDP of 1980 is 1334.56%. If someone takes into account the projections indicated by the red color, the average increase for the 34 year period is 1773.29%.

The PPC S.A. (2009) generates 85% of the country’s electricity and holds 91% of lignite exploitation rights in Greece. Despite the liberalization of the electricity wholesale market which started in 2001, the PPC continues to enjoy a monopoly over access to lignite (EUbusiness 2009). Partially affected by this phenomenon, the price of electricity

in Greece started to rise after the so-called liberalization (RIZOSPASTIS 2002; NAFTEMPORIKI 2005; RAE 2007; Media2Day Publishing S.A. 2007; iNews.gr 2009).

The emissions of carbon dioxide are a parameter that can introduce an additional cost for every country. This is because the emissions that occur from the consumption of fossil fuels, provided that they are greater than a specific limit, are accompanied by a certain amount of money that will have to be submitted by the pollutant.

According to the most recent plan for the emissions rights from 2008 to 2012, the following limits/emissions rights are attributed to the sector of electricity generation (Voutsadakis 2008):

Total quantity: 328 million tones of carbon dioxide

Electricity generation sector: 230 million tones of carbon dioxide

PPC: 220 million tones of carbon dioxide

If the structure of the electricity generation sector in Greece remains the same, beginning from 2013, 2.2 billion euros will have to be submitted for purchasing additional emissions rights. This is equal to 35 more euros for every MWh produced. Consequently the price of electricity will increase by approximately 45% compared to the current prices (Voutsadakis 2008).

As mentioned in section 1, numerous parameters, some of which can easily be quantified, have to be taken into account in order to form future scenarios concerning the price of energy. Godet in “La Prospective” (Godet 1982) emphasizes on the lack of a global and qualitative approach concerning the forecasting process.

Although quantitative methods are characterized by a high reliability, especially in short term forecasts, in the case of long time periods quantitative methods present many blind spots. This is justified by the fact that in a long time period, the probability for a person to face unexpected events is rather high. For this reason, it is not proper to depend only on mathematical methods to make forecasts. What is needed is a combination of both quantitative and qualitative methods. This is exactly what Godet (1982) proposes in his written work. Price of energy can not be cut off from the existence of phenomena, that even though can not be easily quantified, do affect in a significant level any action related to the energy scheme.

For this reason, I totally accept the view of Godet, according to which forecasting – apart from numbers – should be based partially on assumptions, insight and judgment; each one of these factors depend a lot on the opinion of the individual.

Experience curves can make accurate estimates about the evolution of technology cost. This requires the inclusion of the methodology limitations. Once the limitations are taken into account, experience curves can be an effective tool for every private or public firm dealing with technology issues.

Experience curves describe the relation between past costs and cumulated production. This way it is possible for someone to estimate future costs through the extrapolation method.

An important observation is that in order to obtain reliable experience curves, it is essential to apply the specific method to a wide set of cost-cumulative production numbers. Otherwise, the forecasts will be of low reliability.

On the other hand, even when the learning curve is evaluated over a wide range of data, relatively dissimilar fits of the same set of numbers are possible to occur; each one of them is equally justifiable.

The conclusion is that the output of a process can be described as the total of a procedure through which experience is gained and one for which no cost reductions occur. The experience gained from individual components explains why experience curves usually bend towards the horizontal axis; this indicates the slowdown of the cost change when a technology matures (Ferioli et al. 2009).

The use of experience curves based on a single country’s data might designate many difficulties and uncertainties. When dealing with specific sectors of the energy market (e.g. wind energy) a global industry analysis is more useful; especially when someone is trying to forecast global cost issues related to the energy market (Junginger 2001).

As mentioned in the first paragraph of this section, limitations of experience curves have to be taken into account. First of all, let’s mention the advantages of this tool (Neij et al. 2003).

1. Experience curves describe how cost declines with cumulative production; the curve emphasizes the need of experience to realize cost reductions. It clearly illustrates that RD&D programs cannot stand alone.

2. Experience curves can be used as a support in strategic decisions.

3. Experience curves can be used to analyze the effect of combined policy measures in terms of installed units and cost reductions.

4. Experience curves can be used to investigate the existence of national and international systems through which experience is possible to be obtained.

5. Experience curves can assist many individuals, such as financial analysts, industry, researchers and policy makers, in analyzing and assessing strategies and policy measures.

Correspondingly, the limitations of the experience curves are the following (Neij et al. 2003):

The success of this method depends on high-quality data. Unfortunately, uncertainty is an element that dominates in the international literature.

Constructing trustworthy experience curves requires a basic understanding of the technology in question. In order to avoid misinterpretation during data analysis, specialists should be asked for their opinion before drawing any conclusions from experience curve analysis.

Experience curves are a tool that must be combined with other methods of analysis of cost reduction sources. Even when a vast amount of data is available, it is possible to draw just conclusions on an aggregated level. For a detailed analysis, other data and tools are required.

Experience curves do not show the effects of individual parameters, but the combined effect of several elements. The analysis of individual parameters requires additional analysis tools.

The limitations of the experience curves arise from data availability. Due to their limitations complementary methods should also be taken into account. Experience curves should be considered as a generic tool for energy technology analysis.

Technological forecasting is an effective tool in setting technology strategies. A large number of techniques have been evolved for technological forecasting. The quality of forecasts depends on the selected techniques. The selection can affect the accuracy and reliability of the forecast.

According to Levary and Han (1995), a good choice of forecasting method should be based on the following factors:

* Data availability

* Degree of data validity

* Number of variables affecting technology development

* Degree of similarity between proposed technology and existing technologies

According to the international literature, the elements that reduce the efficiency and accuracy of technological forecasting are the following (Mishra et al. 2002):

Insufficiencies of Technological Forecasting

I. Limitations of Quantitative Techniques

· Adaptability to current rate of technology change is low.

* Many degrees of freedom in a rapid changing environment are difficult to address.

* Complex mathematical models are difficult to comprehend and practice.

* Accuracy, stability and reliability are negatively affected by long-term forecasts.

* Lack of adequate past data.

II. Limitations of Qualitative Techniques (Exploratory)

· Definition and selection of experts is difficult.

* Individual bias enters into subjective assessment methods.

* Validity of assumptions in scenario development tends to be uncertain with time.

* Social, political and economic factors are usually neglected.

* Sharp disruptions in trends and unexpected events are usually avoided.

* Forecasts are sometimes carried out by someone who belongs to a specific organization.

III. Limitations of Qualitative Techniques (Normative)

· The chosen data may be conveniently selected to fit a particular technology.

* Technology at the bottom of the tree may be preconceived.

IV. Human Related Problems with Forecasters

* Selection of forecasters team is not correct.

* There is a tendency to avoid information gathering and scanning prior to forecast.

* Some forecasters lack imagination and/or nerve.

V. Problems in Selection of Techniques

* Monitoring prior to selection is lacking.

* Validation of one technique by another is not carried out to reduce subjectivity.

* Techniques do not incorporate performance measures.

* Failures are not considered during the forecasts.

According to the previous issues, the process of forecasting will deliver low reliability results if the limitations are not restricted. In order to improve the accuracy of technological forecasts, one of the issues that need to be addressed is the proper selection of technique. Apart from this, the complexity of technology has to be taken into account as well. Finally, in order to increase the validity of the results of the selected technique, it is recommended to use another technique; this will increase the reliability of the forecast (Mishra et al. 2002).

It is generally accepted between forecast researchers that the combination of methods improves forecast accuracy (Mackay and Metcalfe 2002).

Experts having access to high quality data and by taking into account the previously mentioned issues are able to make relatively high accuracy forecasts concerning the evolution in the sector of technology.

On the other hand, someone may wonder how come people with no expertise in a specific technological sector, have the ability to make, most of the time, an almost accurate forecast about the technological changes that will occur in the future. There are many experiments carried out in various scientific areas that justify the ability of non-experts to make an accurate prediction. A research carried out by Austin Grigg, involved specialists, trainees and people with little to none expertise. The result of the experiment was the negligible difference (in favor of the experts) in the accuracy of the prediction between the specialists and the people with little knowledge (Armstrong 1980).

This phenomenon is justified by the fact that people who do not have the necessary experience and tools for a scientific forecast, depend mainly on their insight (including myself) when they are asked to make a prediction.

In the third section of this assessment project, the importance of the qualitative parameters was accepted. The most significant part of the qualitative element in a forecasting process is the insight. It’s about the ability to predict something, not by depending on mathematical models and numbers, but on the identification of relationships and behaviors within a model, context, or scenario (Reay 2009).

Therefore, when an individual is fully aware of the interactions among the qualitative parameters, that affect the evolution of an under-study issue and the issue itself, then a prediction carried out by this individual will have a high reliability degree, concerning the proper use and explanation of the qualitative data.

The chosen case study for this section is the California electricity crisis of 2000 and 2001. The deregulation of the electricity market in California was expected to reduce the high retail prices of electricity. The result was exactly the opposite compared to the initial purpose; wholesale prices increased even more, customers experienced interruptions in supply of energy and utilities bankrupted. Among the crisis roots, the following factors are included:

· The absence of additional generating capacity.

· The unexpected dry season and spikes in natural gas prices (California was greatly depended on the operation of hydro plants and natural gas).

· The market infrastructure allowed power generation firms to control wholesale prices in the power exchange market.

· The delay and inability of regulators to predict the crisis and appropriate respond to this phenomenon.

In 4.2, someone may observe that power plant outages increased during the crisis period; this affected in a high degree the magnitude of blackouts that customers experienced. The yellow strip bars indicate an average increase of 8759 MW for the months January, March and May of 2001 compared to the corresponding months of 1999 and 2000.

According to 7, the dawn of deregulation indicates a rather smooth trend concerning the evolution of wholesale market prices; what was expected to occur had nothing to do with the sharp increase of prices that took place after that period.

The increase in retail prices ( 8-2001 to 2003) was not expected, either because the deregulation was expected to establish low prices or because past data indicated that low prices ( 8-red dots) were likely to occur in the years to come.

A key point, totally irrelevant to statistical data and concerning the flaws of forecasting techniques applied before the incident of California, will be analyzed in the next lines.

When the State and the regulators of California implemented the deregulation system that was successfully adopted first by other countries (Woo et al. 2003), they proceeded in specific changes (e.g. imposition of retail price caps, partial deregulation) without trying to carry out a detailed study about the complex Californian energy scheme. In other words, what might had been successful somewhere else, it was considered as a guaranteed success in the case of California. Additionally, if the adjustment of energy market infrastructure and potential manipulations had been taken into account by examining the validity of alternative scenarios concerning the risk of deregulating the Californian electricity market, certain mechanisms able to respond to a future crisis would have probably been developed.

According to the previous comments, it is obvious that forecasting methods that rely exclusively on historical data trends, they ignore the opinion of individuals and do not take into account the conditions that prevail in the environment where the under-analysis phenomenon develops. Thus, any forecasts made are of low validity and reliability.

The following table contains the data for the installed nuclear capacity between 1965 and 1985. These data have been highlighted with the green color.

Table 2: (Source: Nuclear Energy Agency n.d.).

Years

Installed Capacity (GW)

1965

5

1970

16

1975

72

1980

136

1985

253

1990

326

1995

345

2000

352

2002

359

2005

368

By using the available data of table 2, the period 1965-1985 will be used as a reference time (instead of 1967-1987). Respectively, predictions will be made for the period 1990-2005.

Forecast method: Trend

Source: Excel

Years

Installed Capacity (GW)

1965

5

1970

16

1975

72

1980

136

1985

253

1990

281.20

1995

342.80

2000

404.40

2002

429.04

2005

466.00

Forecast method: Linear extrapolation

Source: Excel

Years

Installed Capacity (GW)

1965

5

1970

16

1975

72

1980

136

1985

253

1990

280.80

1995

342.40

2000

404.00

2002

428.64

2005

465.60

Forecast method: Logarithmic extrapolation

Source: Excel

Years

Installed Capacity (GW)

1965

5

1970

16

1975

72

1980

136

1985

253

1990

282.64

1995

343.67

2000

404.55

2002

428.86

2005

465.28

Forecast method: Polynomial extrapolation

Source: Excel

Years

Installed Capacity (GW)

1965

5

1970

16

1975

72

1980

136

1985

253

1990

≈ 400

1995

≈ 590

2000

≈ 790

2002

≈ 900

2005

≈ 1000

It is obvious that among all the extrapolation-forecasting techniques, the one with the lowest divergence from the actual data is the logarithmic extrapolation method followed by the linear technique.

On the other hand, none of the used methods was able to give high accuracy forecasts because the nuclear installed capacity evolution was highly affected by the incident of Chernobyl. The various mathematical methods can not express the fear of the scientific community to abandon the development of nuclear sector.

According to 9, knowing that the incident of Chernobyl occurred in 1986, it is obvious that the rapid increase in nuclear installed capacity started to decline approximately 4 years (1990) after the incident of Chernobyl.

Based on 10 and table 3, someone may conclude that the energy balance in Europe is highly depended on the operation of Gazprom.

Table 3: (Source: U.S. Energy Information Administration 2008).

Country

2006 % of Domestic

NG Consumption

Germany

37%

Turkey

64%

Italy

25%

France

20%

Czech Republic

79%

Poland

47%

Hungary

54%

Slovakia

100%

Austria

74%

Finland

100%

Romania

28%

Bulgaria

96%

Greece

82%

Serbia & Montenegro

87%

Croatia

37%

Slovenia

64%

Switzerland

12%

Skopje

100%

According to table 3, Greece dependency on Gazprom’s natural gas is approximately 82%. This share indicates that a potential crisis in natural gas supply could lead to significant problems in sectors such as domestic heating and gas-fired power plants.

Greece as a major energy user

Greece has insignificant domestic reserves of oil and gas and relies greatly on energy imports. In 2006, the total energy consumption was 1.4 Quadrillion Btu. The distribution of this amount of energy can be located on 11.

Although the share of natural in total energy consumption is not very high ( 11), the high dependency degree on Gazprom supply and the decreasing reserves of Greece in natural gas, would result in significant impacts in the energy balance of Greece. Provided that I would be somehow involved in the coordination of the energy planning of my country, I would propose the following measures.

1. Greece already has one natural gas import terminal situated at Revithoussa (DESFA 2007). The supplier is the Algerian company Sonatrach. The first thing that someone would have to do is to estimate the additional quantity that this company could provide to Greece on an annual basis. Then, proceed in reducing the imported natural gas from Gazprom by the same amount.

2. The countries currently facing problems with the natural gas supply from Russia are Bulgaria, Croatia, Greece, Skopje, Romania and Turkey. Partial supply decreases have also been reported by Austria (90%), Slovakia (70%), the Czech Republic (75%) and Hungary. Another solution to the problem could be the restart of Unit 3 at the Kozloduy Nuclear Power Plant. Kozloduy-3 was shutdown in December 2006 as part of an agreement with the European Union, which was concerned about inadequate safety levels. However, Bulgaria’s EU accession treaty apparently allows closed reactors to be temporarily re-started in the event of an acute energy shortage (Resnicoff 2009).

3. Rational use of energy, especially in sectors being supplied by natural gas, would be the last recommendation by my side.

9)

The first and the third proposed measure can be characterized as a necessary but conservative approach. The proposed energy policy of Greece towards an energy crisis must ensure the energy security of the country. Taking into consideration the great dependence on energy imports in Greece, the “out of the box” solutions seems to be enough risky.

More precisely, the improvement on the penetration of RES is a conservative and safe solution. Additionally energy efficiency and rational use of energy can be described as an incremental approach too. The “out of the box” solutions in the proposed response include new natural gas suppliers and improvement on energy storage capacity. The first solution is possible to lead to disturbances between Greece and Russia and the second one might require a large capital invested in various storage methods.

Regardless the type of the response (“out of the box” or conservative-incremental approach) that someone might propose, both approaches have their merits and limitations. The adoption of a conservative approach offers a sense of security. Usually this kind of solutions can be accomplished easily due to the fact that includes measures that have been tested in previous similar situations. Also there are various limitations for this kind of approach. For instance, the increase of RES in Greek fuel mix has the following barriers. The legal framework and the authorization procedure can be described as complex procedures. In many cases this can frustrate many small investors. Another limitation is the inhibitive cost for the interconnection to the grid (mostly for reinforcement or construction of new network lines). Additionally for larger stations (more than _20MW) and in certain areas with very high wind potential there is lack of sufficient High Voltage (HV) system capacity. Due to environmental restrictions and local community protests, expansion of the HV system is in some cases completely blocked. Finally, in the case of wind farms, public acceptability is also an issue in certain cases, basically due to visual impact or other reasons (Hatziargyriou, 2007). The adoption of an “out o box” solution in the case of an energy crisis might be risky. For this reason, this type of approach could be followed by conservative solutions ensuring that the energy security of the country will remain sufficient in any case. As it is mentioned before, Greece began receiving gas from Azerbaijan and the relative imports will likely increase as the Turkey-Greece interconnector is further utilized. This can be described as an “out o box” solution that can lead to imbalances between Greece and Russia, reducing the amount of Russian natural gas in Greece. On the other hand, the competition among various natural gas suppliers will have a positive effect on the corresponding energy prices. To sum up, both approaches have advantages and disadvantages. The selection of an appropriate crisis management scheme is a complicate procedure and requires mature choices taken by veteran politicians. In addition an “out o box” solution for Greece may be a conservative – incremental approach for another country that is an energy exporter or a country that has a stronger economy or better international relationships than Greece.

10) References

Alam, S.M. (2006). Economic Growth With Energy [online]. [Accessed 24 December 2009]. Available from: .

Armstrong, S.J. (1980). The Seer-Sucker Theory: The Value of Experts in Forecasting [online]. [Accessed 1 January 2010]. Available from: .

Cabral, L. (2002). The California energy crisis. Japan and the World Economy [online]. 14(3), [Accessed 5 January 2010], p.335-339. Available from: .

California Energy Commission (2009). Utility-wide Weighted Average Retail Electricity Prices [online]. [Accessed 6 January 2010]. Available from: .

Consumer Watchdog (2002). How Deregulation Let the Power Industry Steal $71 Billion From California [online]. [Accessed 5 January 2010]. Available from: .

DESFA (2007). Liquefied Natural Gas Terminal at Revythousa [online]. [Accessed 7 January 2010]. Available from: .

Energy Information Administration (2009). Greece Energy Data, Statistics and Analysis – Oil, Gas, Electricity, Coal [online]. [Accessed 7 January 2010]. Available from:


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