Comparing the forecasting performances of linear models for electricity prices with high RES penetration

We compare alternative univariate versus multivariate models and frequentist versus Bayesian autoregressive and vector autoregressive specifications for hourly day-ahead electricity prices, both with and without renewable energy sources. The accuracy of point and density forecasts is inspected in four main European markets (Germany, Denmark, Italy, and Spain) characterized by different levels of renewable energy power generation. Our results show that the Bayesian vector autoregressive specifications with exogenous variables dominate other multivariate and univariate specifications in terms of both point forecasting and density forecasting.     

Forecasting electricity prices using bid data

Market liberalization and the expansion of variable renewable energy sources in power systems have made the dynamics of electricity prices more uncertain, leading them to show high volatility with sudden, unexpected price spikes. Thus, developing more accurate price modeling and forecasting techniques is a challenge for all market participants and regulatory authorities. This paper proposes a forecasting approach based on using auction data to fit supply and demand electricity curves. More specifically, we fit linear (LinX-Model) and logistic (LogX-Model) curves to historical sale and purchase bidding data from the Iberian electricity market to estimate structural parameters from 2015 to 2019. Then we use time series models on structural parameters to predict day-ahead prices. Our results provide a solid framework for forecasting electricity prices by capturing the structural characteristics of markets.     

A refined parametric model for short term load forecasting

We present a refined parametric model for forecasting electricity demand which performed particularly well in the recent Global Energy Forecasting Competition (GEFCom 2012). We begin by motivating and presenting a simple parametric model, treating the electricity demand as a function of the temperature and day of the data. We then set out a series of refinements of the model, explaining the rationale for each, and using the competition scores to demonstrate that each successive refinement step increases the accuracy of the model’s predictions. These refinements include combining models from multiple weather stations, removing outliers from the historical data, and special treatments of public holidays.     

Using weather ensemble predictions in electricity demand forecasting

Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electricity demand. The results show that the average of the demand scenarios is a more accurate demand forecast than that produced using traditional weather forecasts. We use the distribution of the demand scenarios to estimate the demand forecast uncertainty. This compares favourably with estimates produced using univariate volatility forecasting methods.     

Improving Electricity Market Price Forecasting with Factor Models for the Optimal Generation Bid

In liberalized electricity markets, the electricity generation companies usually manage their production by developing hourly bids that are sent to the day‐ahead market. As the prices at which the energy will be purchased are unknown until the end of the bidding process, forecasting of spot prices has become an essential element in electricity management strategies. In this article, we apply forecasting factor models to the market framework in Spain and Portugal and study their performance. Although their goodness of fit is similar to that of autoregressive integrated moving average models, they are easier to implement. The second part of the paper uses the spot‐price forecasting model to generate inputs for a stochastic programming model, which is then used to determine the company's optimal generation bid. The resulting optimal bidding curves are presented and analyzed in the context of the Iberian day‐ahead electricity market.     

Residential energy demand in the United States: A regional econometric analysis

A static equilibrium and a dynamic partial adjustment model of residential demand for electricity and natural gas are presented and estimated for the United States over a recent period characterized by sharply increasing energy prices. The static model is estimated using Ordinary Least Squares while the instrumental variables method is used for the dynamic partial adjustment model. The estimates of long-run elasticities suggest the residential demand for electricity and natural gas are price and income elastic. Intercept and slope dummies used in the models identify significant regional differences in demand functions.     

Enhanced network effects and stochastic modelling in generation expansion planning: Insights from an insular power system

Electricity generation capacity expansion is driven by both economic and socio-political realities. Policy makers determine public infrastructural decisions, such as climate and renewable targets, and transmission infrastructure, and the optimal generation capacity expansion follows. Policy makers therefore require planning models that can determine the optimal generation capacity mix in the long run under various scenarios, including policy choices. This work presents a planning model based on linearised alternating current optimal power flow which determines optimal generation capacity expansion and operation, in a least-cost manner, given global and local technical constraints, as well as policy decisions. We apply the model to a test case of the island of Ireland, which has two weakly interconnected systems, high renewable generation targets and low storage and interconnection. We determine the optimal generation expansion and operation out to 2030 considering the effects of increased multi-area interconnection, existing fossil fuel generation phase-out and increased renewable generation targets and carbon prices. Our results find that costs and emissions are driven primarily by the decommissioning of old inefficient generation units. High renewable targets, on the other hand, render increased carbon prices relatively ineffective in reducing system emissions. Furthermore, high renewable generation targets crowd out low-carbon power generation options such as carbon capture and storage (CCS). The strategic north-south interconnection has little effect on renewable energy source installations required to achieve renewable power generation targets but does impact on security of supply and the congestion level across the island.     

Mixed random forest,cointegration, and forecasting gasoline prices

One of the most successful forecasting machine learning (ML) procedures is random forest (RF). In this paper, we propose a new mixed RF approach for modeling departures from linearity that helps identify (i) explanatory variables with nonlinear impacts, (ii) threshold values, and (iii) the closest parametric approximation. The methodology is applied to weekly forecasts of gasoline prices, cointegrated with international oil prices and exchange rates. Recent specifications for nonlinear error correction (NEC) models include threshold autoregressive models (TAR) and double-threshold smooth transition autoregressive (STAR) models. We propose a new mixed RF model specification strategy and apply it to the determinants of weekly prices of the Spanish gasoline market from 2010 to 2019. In particular, the mixed RF is able to identify nonlinearities in both the error correction term and the rate of change of oil prices. It provides the best weekly gasoline price forecasting performance and supports the logistic error correction model (ECM) approximation.     

Determinants of price mark‐up tolerance for green electricity – lessons for environmental marketing strategies from a study of residential electricity customers in Germany

This paper develops hypotheses on the effects of various attitudinal and perceptual variables as well as socio‐demographic characteristics of residential electricity customers on an individual's willingness to pay a mark‐up for electricity generated from renewable energy sources compared with the price due for electricity from conventional sources. The hypotheses are tested with data from a standardized telephone survey of 238 household electricity consumers in Germany. 53.4% of the participants are willing to pay a mark‐up for green electricity. 26.1% report a price tolerance equal to a 5–10% increase in their current electricity bill. Binary logistic and ordinal regression analyses indicate that price tolerance for green electricity is particularly influenced by attitudes (1) towards environmental issues and (2) towards one's current power supplier, (3) perceptions of the evaluation of green energy by an individual's social reference groups, (4) household size and (5) current electricity bill level. The findings are used to derive suggestions for energy related informational activities of public institutions, green marketing strategies of energy companies and future consumer research regarding demand for pro‐environmental goods. Copyright © 2009 John Wiley & Sons, Ltd and ERP Environment.     

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.     

Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?

Recent electricity price forecasting studies have shown that decomposing a series of spot prices into a long-term trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same regression or neural network model is calibrated to the prices themselves. Here, considering two novel extensions of this concept to probabilistic forecasting, we find that (i) efficiently calibrated non-linear autoregressive with exogenous variables (NARX) networks can outperform their autoregressive counterparts, even without combining forecasts from many runs, and that (ii) in terms of accuracy it is better to construct probabilistic forecasts directly from point predictions. However, if speed is a critical issue, running quantile regression on combined point forecasts (i.e., committee machines) may be an option worth considering. Finally, we confirm an earlier observation that averaging probabilities outperforms averaging quantiles when combining predictive distributions in electricity price forecasting.     

A POST-KEYNESIAN AGE MODEL TO FORECAST ENERGY DEMAND IN SPAIN

This paper develops an extended input–output model for the estimation of energy demand and related issues. It is built on the last Spanish Symmetric Input–Output Table (IOT, 2005). It has been tested for the period 2005–2008 and used for forecasting energy demand for the years 2009–2012 under different economic scenarios. The model shares some traits of the computable and applied general equilibrium models where quantity and price systems are interwoven. The differences lie in the theories explaining output and prices. Our quantity system is based on Keynes’ principle of effective demand (broad energy multipliers are derived). The price system is based on the classical (Sraffian) theory of prices of production, akin to post-Keynesian full-cost prices. The general price system can be manipulated to account for the specificities of energy prices. Historical trends of energy coefficients are computed by extrapolation of past IOTs and calibration.     

Do global output gaps help forecast domestic inflation? Evidence from Phillips curves for Poland

This study confronts domestic and global views on inflation through the use of the Hybrid New Keynesian Phillips Curve (HNKPC) models estimated for headline and core inflation in Poland. We analyse the roles of the global vs. domestic output gaps in affecting price changes. We ensure that our conclusions are robust by taking into consideration various proxies for inflation expectations, imported inflation, the domestic output gap and the global output gap.Our results suggest that the global demand conditions are statistically insignificant in the majority of the estimated global versions of HNKPC, independently of the measure of them that is considered. In terms of empirical fit, and especially of the out-of-sample forecasting accuracy, the specifications of the Phillips curve with the domestic and global output gaps among the explanatory variables are not superior to traditional Phillips curves. Interestingly, the relative importance of the global output gap is much smaller in models that are estimated in terms of core inflation, excluding foodstuffs and energy, than in CPI inflation models. This suggests that global demand conditions affect the inflation in Poland indirectly, mainly through the prices of food and energy raw materials.The main conclusion from our study is that external factors that are already considered in the traditional hybrid versions of the new Keynesian Phillips curve are sufficient to account for global influences on prices in the domestic economy. The concept of the global output gap improves neither the explanatory nor the predictive power of HNKPC models.     

Rethinking weather station selection for electric load forecasting using genetic algorithms

Demand forecasting is and has been for years a topic of great interest in the electricity sector, being the temperature one of its major drivers. Indeed, one of the challenges when modelling the load is to choose the right weather station, or set of stations, for a given load time series. However, only a few research papers have been devoted to this topic. This paper reviews the most relevant methods that were applied during the Global Energy Forecasting Competition of 2014 (GEFCom2014) and presents a new approach to weather station selection, based on Genetic Algorithms (GA), which allows finding the best set of stations for any demand forecasting model, and outperforms the results of existing methods. Furthermore its performance has also been tested using GEFCom2012 data, providing significant error improvements. Finally, the possibility of combining the weather stations selected by the proposed GA using the BFGS algorithm is briefly tested, providing promising results.     

谈电力垄断行业的改革

电力垄断行业的改革对于我国电力行业的发展具有重要意义。这就要求相关部门人员从多个方面考虑,实现水电等可再生能源的发展,提高能源的利用效率,以此来为我国社会发展创造一个崭新的电力行业。     

Appraising world energy prospects

The authors begin by outlining a multi-scenario technique for coping with future uncertainty in assessments of the business environment for energy planning. The discussion then leads to a quantification of world energy demand under two exploratory scenarios, whose results are compared with published forecasts. Analysis of the components of demand highlights the importance of the Third World. After a review of the world's fossil fuel resources, the likely effective availability of oil and other energy sources (including nuclear power and the renewables) is set against the scenario levels of energy demand. The paper ends with a summary of the implications for action in the field of energy.     

Behavior of the firm under rate-of-return regulation with two capital inputs

Traditional electric utility companies face a trade-off between building generation facilities that utilize renewable energy (RE) and non-renewable energy (non-RE). The firm's input decision to build capacity for either source depends on several constraining factors, including input prices, policies that promote or discourage RE use, and the type of regulation faced by the firm. This paper models the utility company's decision between RE and non-RE capital inputs. From the model, we derive the result that rate-of-return (ROR) regulation decreases the investment in RE capital relative to the unregulated firm. These findings suggest restructuring electricity generation markets, which removes the ROR on generating assets, can increase the relative use of RE. A second result of the model shows that the renewable portfolio standard (RPS) increases the investment in capital that requires RE as a source of electricity, as expected. This paper contributes to the literature on the substitution between renewable and non-renewable resources, by examining the policies that affect the investment in the two types of technologies. The model can also be applied to other regulated utilities, such as water or natural gas companies, with outputs that are produced from different types of capital.     

Electricity derivatives pricing with forward-looking information

In order to increase overall transparency on key operational information, power transmission system operators publish an increasing amount of fundamental data, including forecasts of electricity demand and available capacity. We employ a fundamental model for electricity prices which lends itself well to integrating such forecasts, while retaining ease of implementation and tractability to allow for analytic derivatives pricing formulae. In an extensive futures pricing study, the pricing performance of our model is shown to further improve based on the inclusion of electricity demand and capacity forecasts, thus confirming the general importance of forward-looking information for electricity derivatives pricing. However, we also find that the usefulness of integrating forecast data into the pricing approach is primarily limited to those periods during which electricity prices are highly sensitive to demand or available capacity, whereas the impact is less visible when fuel prices are the primary underlying driver to prices instead.     

Big data from dynamic pricing: A smart approach to tourism demand forecasting

Suppliers of tourist services continuously generate big data on ask prices. We suggest using this information, in the form of a price index, to forecast the occupation rates for virtually any time-space frame, provided that there are a sufficient number of decision makers “sharing” their pricing strategies on the web. Our approach guarantees great transparency and replicability, as big data from OTAs do not depend on search interfaces and can facilitate intelligent interactions between the territory and its inhabitants, thus providing a starting point for a smart decision-making process. We show that it is possible to obtain a noticeable increase in the forecasting performance by including the proposed leading indicator (price index) into the set of explanatory variables, even with very simple model specifications. Our findings offer a new research direction in the field of tourism demand forecasting leveraging on big data from the supply side.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.