Electricity price forecasting: A review of the state-of-the-art with a look into the future

Weron, Rafał

doi:10.1016/j.ijforecast.2014.08.008

Cited by 1,268 publications

(938 citation statements)

References 285 publications

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“…Therefore, we start with the simplest models such as AR (1) but try also a four-week, i.e., monthly, seasonality (Weron, 2014). Table 15 reports the model iterations.…”

Section: Weekly Volatilitymentioning

confidence: 99%

Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany

Rintamäki¹,

Siddiqui

Salo³

2017

Energy Economics

167

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Although variable renewable energy (VRE) technologies with zero marginal costs decrease electricity prices, the literature is inconclusive about how the resulting shift in the supply curves impacts price volatility. Because the flexibility to respond to high peak and low offpeak prices is crucial for demand-response applications and may compensate for the losses of conventional generators caused by lower average prices, there is a need to understand how the penetration of VRE affects volatility. In this paper, we build distributed lag models with Danish and German data to estimate the impact of VRE generation on electricity price volatility. We find that in Denmark wind power decreases the daily volatility of prices by flattening the hourly price profile, but in Germany it increases the volatility because it has a stronger impact on off-peak prices. Our analysis suggests that access to flexible generation capacity and wind power generation patterns contribute to these differing impacts. Meanwhile, solar power decreases price volatility in Germany. By contrast, the weekly volatility of prices increases in both areas due to the intermittency of VRE. Thus, policy measures for facilitating the integration of VRE should be tailored to such region-specific patterns.

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“…Therefore, we start with the simplest models such as AR (1) but try also a four-week, i.e., monthly, seasonality (Weron, 2014). Table 15 reports the model iterations.…”

Section: Weekly Volatilitymentioning

confidence: 99%

Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany

Rintamäki¹,

Siddiqui

Salo³

2017

Energy Economics

167

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“…Of course, the ETS method simply decomposes a time series into trend, seasonal, and error components without taking other elements into account, such as cyclical movements. As for ARIMA models, it might be a good idea to add exogenous variables to the model [40,41]. Furthermore, it is also worthwhile to apply equilibrium, structural, and reduced form models in forecasting the prices of Japanese logs.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Monthly Prices of Japanese Logs

Michinaka¹,

Kuboyama²,

Tamura³

et al. 2016

Forests

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Forecasts of prices can help industries in their risk management. This is especially true for Japanese logs, which experience sharp fluctuations in price. In this research, the authors used an exponential smoothing method (ETS) and autoregressive integrated moving average (ARIMA) models to forecast the monthly prices of domestic logs of three of the most important species in Japan: sugi (Japanese cedar, Cryptomeria japonica D. Don), hinoki (Japanese cypress, Chamaecyparis obtusa (Sieb. et Zucc.) Endl.), and karamatsu (Japanese larch, Larix kaempferi (Lamb.) Carr.). For the 12-month forecasting periods, forecasting intervals of 80% and 95% were given. By measuring the accuracy of forecasts of 12-and 6-month forecasting periods, it was found that ARIMA gave better results than did the ETS in the majority of cases. However, the combined method of averaging ETS and ARIMA forecasts gave the best results for hinoki in several cases.

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“…To motivate this choice, we first compare briefly with the typical structural modeling approach of simplifying the stack's shape via parametric functions. A broad range of parametric structural models has been introduced in the literature, see, e.g., Weron, 2014), which differ in the number of fundamental relationships they choose to capture or the adopted transformation function to capture them. The main component of a structural model is the explicit parametric function to relate the electricity price with the electricity demand, capacity, marginal fuel, or multiple fuels.…”

Section: Price Formation Mechanism and Sensitivity Estimationmentioning

confidence: 99%

“…Furthermore, these models require a simplification of the detailed generation cost structure in the market, for example via smooth-shaped clusters of bids for each fuel type in Coulon and Howison (2009), or via an assumption of equal heat rates within fuel types and a fixed ordering of fuels in Aid et al (2013). While the more sophisticated structural models cause estimation challenges and require significant modifications for different markets, the simpler structural models (like the one in equation (2)) may not be transferrable from one electricity market to another, without substantial effort and care taken to adapt the model to the key differences and identify dominant risk factors, as discussed in the survey papers Weron, 2014).…”

Section: Price Formation Mechanism and Sensitivity Estimationmentioning

confidence: 99%