The importance and accuracy of wind power forecasts

Due to the global crisis o f climate change many countries throughout the world are installing the renewable energy o f wind power into their electricity system. Wind energy causes complications when it is being integrated into the electricity system due its intermittent nature. Additionally winds intennittency can result in penalties being enforced due to the deregulation in the electricity market. Wind power forecasting can play a pivotal role to ease the integration o f wind energy. Wind power forecasts at 24 and 48 hours ahead of time are deemed the most crucial for determining an appropriate balance on the power system. In the electricity market wind power forecasts can also assist market participants in terms o f applying a suitable bidding strategy, unit commitment or have an impact on the value o f the spot price. For these reasons this study investigates the importance o f wind power forecasts for such players as the Transmission System Operators (TSOs) and Independent Power Producers (IPPs). Investigation in this study is also conducted into the impacts that wind power forecasts can have on the electricity market in relation to bidding strategies, spot price and unit commitment by examining various case studies. The results o f these case studies portray a clear and insightful indication o f the significance o f availing from the information available from wind power forecasts. The accuracy o f a particular wind power forecast is also explored. Data from a wind power forecast is examined in the circumstances o f both 24 and 48 hour forecasts. The accuracy o f the wind power forecasts are displayed through a variety o f statistical approaches. The results o f the investigation can assist market participants taking part in the electricity pool and also provides a platform that can be applied to any forecast when attempting to define its accuracy. This study contributes significantly to the knowledge in the area o f wind power forecasts by explaining the importance o f wind power forecasting within the energy sector. It innovativeness and uniqueness lies in determining the accuracy o f a particular wind power forecast that was previously unknown.

Microstructure Order Flow: Statistical and Economic Evaluation of Nonlinear Forecasts

In this paper we propose a novel empirical extension of the standard market microstructure order flow model. The main idea is that heterogeneity of beliefs in the foreign exchange market can cause model instability and such instability has not been fully accounted for in the existing empirical literature. We investigate this issue using two di¤erent data sets and focusing on out- of-sample forecasts. Forecasting power is measured using standard statistical tests and, additionally, using an alternative approach based on measuring the economic value of forecasts after building a portfolio of assets. We nd there is a substantial economic value on conditioning on the proposed models.

Evaluating the usefulness of forecasts of relative growth

Forecasts of differences in growth between countries serve an important role in the justification of governments’ fiscal policy stances, but are not tested for their accuracy as part of the current range of forecast evaluation methods. This paper examines forecasted and outturn growth differentials between countries to identify if there is usefulness in forecasts of “relative” growth. Using OECD forecasts and outturn values for GDP growth for (combinations of) the G7 countries between 1984 and 2010, the paper finds that the OECD’s success in predicting the relative growth of G7 countries during this period is good. For each two-country combination results indicate that relative growth forecasts are less useful for countries which have smaller outturn growth differentials.

Quantile forecasts of inflation under model uncertainty

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for different predictors to affect different quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future inflation by providing superior predictive densities compared to mean regression models with and without BMA.

Traffic forecasts under uncertainty and capacity constraints

Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This paper quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the paper proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. This methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires.

Preliminary estimates and forecasts of the population prevalence of self reported of hypertension, of ischaemic heart disease and stroke in the Republic of Ireland from 2005 - 2015

Ireland and Northern Ireland’s Population Health Observatory (INIsPHO) recently published estimates of the population prevalence of diabetes in 2005 and forecasts to 2010 and 2015 for the island of Ireland, at the national and sub-national levels. These estimates are based the PBS Model developed by York and Humber Public Health Observatory (YHPHO), Brent NHS Trust and the School of Health and Related Research (ScHARR).The Department of Health and Children (DoHC) has requested additional estimates and forecasts for hypertension.This paper outlines the results from preliminary work from the initial steps towards a more systematic approach to monitoring the prevalence of other chronic diseases on the island.

Combining multivariate density forecasts using predictive criteria

This paper combines multivariate density forecasts of output growth, inflationand interest rates from a suite of models. An out-of-sample weighting scheme based onthe predictive likelihood as proposed by Eklund and Karlsson (2005) and Andersson andKarlsson (2007) is used to combine the models. Three classes of models are considered: aBayesian vector autoregression (BVAR), a factor-augmented vector autoregression (FAVAR)and a medium-scale dynamic stochastic general equilibrium (DSGE) model. Using Australiandata, we find that, at short forecast horizons, the Bayesian VAR model is assignedthe most weight, while at intermediate and longer horizons the factor model is preferred.The DSGE model is assigned little weight at all horizons, a result that can be attributedto the DSGE model producing density forecasts that are very wide when compared withthe actual distribution of observations. While a density forecast evaluation exercise revealslittle formal evidence that the optimally combined densities are superior to those from thebest-performing individual model, or a simple equal-weighting scheme, this may be a resultof the short sample available.

CJJP Forecasts Increase in Special Sentence Sex Offenders Caseloads, Data Download, Iowa Department of Corrections, December 2011

The Iowa Sex Offender Research Council recently released a report to the Iowa General Assembly focusing on sex offender registration and the special sentence for sex offenders. Regarding the latter, the Council (staffed by the state’s Division of Criminal & Juvenile Justice Planning) projected a steady increase in community-based corrections’ special sentence caseloads from 619 offenders in 2011 to 2,651 offenders in 2021:

A probabilistic approach to niche-based community models for spatial forecasts of assemblage properties and their uncertainties

Aim Conservation strategies are in need of predictions that capture spatial community composition and structure. Currently, the methods used to generate these predictions generally focus on deterministic processes and omit important stochastic processes and other unexplained variation in model outputs. Here we test a novel approach of community models that accounts for this variation and determine how well it reproduces observed properties of alpine butterfly communities. Location The western Swiss Alps. Methods We propose a new approach to process probabilistic predictions derived from stacked species distribution models (S-SDMs) in order to predict and assess the uncertainty in the predictions of community properties. We test the utility of our novel approach against a traditional threshold-based approach. We used mountain butterfly communities spanning a large elevation gradient as a case study and evaluated the ability of our approach to model species richness and phylogenetic diversity of communities. Results S-SDMs reproduced the observed decrease in phylogenetic diversity and species richness with elevation, syndromes of environmental filtering. The prediction accuracy of community properties vary along environmental gradient: variability in predictions of species richness was higher at low elevation, while it was lower for phylogenetic diversity. Our approach allowed mapping the variability in species richness and phylogenetic diversity projections. Main conclusion Using our probabilistic approach to process species distribution models outputs to reconstruct communities furnishes an improved picture of the range of possible assemblage realisations under similar environmental conditions given stochastic processes and help inform manager of the uncertainty in the modelling results

Automatic Tracer-Dilution Method Used for Stage-Discharge Ratings and Streamflow Hydrographs on Small Iowa Streams, HR-285, 1990

An automatic system was designed to concurrently measure stage and discharge for the purpose of developing stage-discharge ratings and high flow hydrographs on small streams. Stage, or gage height, is recorded by an analog-to-digital recorder and discharge is determined by the constant-rate tracer-dilution method. The system measures flow above a base stage set by the user. To test the effectiveness of the system and its components, eight systems, with a variety of equipment, were installed at crest-stage gaging stations across Iowa. A fluorescent dye, rhodamine-WT, was used as the tracer. Tracer-dilution discharge measurements were made during 14 flow periods at six stations from 1986 through 1988 water years. Ratings were developed at three stations with the aid of these measurements. A loop rating was identified at one station during rapidly-changing flow conditions. Incomplete mixing and dye loss to sediment apparently were problems at some stations. Stage hydrographs were recorded for 38 flows at seven stations. Limited data on background fluorescence during high flows were also obtained.

Statistical Summaries of Selected Iowa Streamflow Data Through September 1996, HR-395, 1998

Statistical summaries of streamflow data collected at 156 streamflow-gaging stations in Iowa are presented in this report. All gaging stations included for analysis have at least 10 years of continuous record collected before or through September 1996. The statistical summaries include (1) statistics of monthly and annual mean discharges; (2) monthly and annual flow durations; (3) magnitudes and frequencies of instantaneous peak discharges (flood frequencies); and (4) magnitudes and frequencies of high and low discharges. Also presented for each gaging station is a graph of the annual mean flows and, for most stations, selected values from the most-recent stage-discharge rating table.

Statistical Summaries of Selected Iowa Streamflow Data Through September 2013, TR-669

This report is the final product of a two-year study that began October 1, 2013. In addition to the funding provided for this study by the Iowa Highway Research Board and the Iowa Department of Transportation (TR-669), the project was also funded by the U.S. Army Corps of Engineers and the U.S. Geological Survey. The report was published as an online report on January 4, 2016. The report is available online at http://dx.doi.org/10.3133/ofr20151214 . The main body of the report provides a description of the statistics presented for the streamgages and an explanation of the streamgage summaries, also included is a discussion of the USGS streamgage network in Iowa. Individual streamgage summaries are available as links listed in table 1, or all 184 streamgage summaries are available in a zipped file named “Streamgage Summaries.”

Vulnerability of stream biota to climate change in mediterranean climate regions: a synthesis of ecological responses and conservation challenges

Freshwater species worldwide are experiencing dramatic declines partly attributable to ongoing climate change. It is expected that the future effects of climate change could be particularly severe in mediterranean climate (med-) regions, which host many endemic species already under great stress from the high level of human development. In this article, we review the climate and climate-induced changes in streams of med-regions and the responses of stream biota, focusing on both observed and anticipated ecological responses. We also discuss current knowledge gaps and conservation challenges. Expected climate alterations have already been observed in the last decades, and include: increased annual average air temperatures; decreased annual average precipitation; hydrologic alterations; and an increase in frequency, intensity and duration of extreme events, such as floods, droughts and fires. Recent observations, which are concordant with forecasts built, show stream biota of med-regions when facing climate changes tend to be displaced towards higher elevations and upper latitudes, communities tend to change their composition and homogenize, while some life-history traits seem to provide biota with resilience and resistance to adapt to the new conditions (as being short-lived, small, and resistant to low streamflow and desiccation). Nevertheless, such responses may be insufficient to cope with current and future environmental changes. Accurate forecasts of biotic changes and possible adaptations are difficult to obtain in med-regions mainly because of the difficulty of distinguishing disturbances due to natural variability from the effects of climate change, particularly regarding hydrology. Long-term studies are needed to disentangle such variability and improve knowledge regarding the ecological responses and the detection of early warning signals to climate change. Investments should focus on taxa beyond fish and macroinvertebrates, and in covering the less studied regions of Chile and South Africa. Scientists, policy makers and water managers must be involved in the climate change dialogue because the freshwater conservation concerns are huge.

Impact of the substitution of reference annual streamflow by monthly streamflow on the potential use of water resources

The consideration of the streamflow seasonality has a high potential to improve the water use. In order to give subsidies to the optimization of water use, it was evaluated the impact of the change of reference annual streamflow by the monthly streamflows in the potential water use throughout the hydrography of Paracatu sub-Basin. It was evaluated the impact on Q7,10 (lowest average streamflow during a 7-day period with an average recurrence of 10 years) and on Q95 (permanent flow present 95% of the time). The use of monthly streamflow to substitute the annual streamflow had a high potential of improvement of water resources use in the sub-Basin studied. The use of monthly Q 7,10 in substitution of annual Q 7,10 increases the potential water use that vary from about 10% in the months of lower water availability to values exceeding 200% in the months with higher availability of surface water resources. The use of monthly Q95 in substitution of the annual Q95 implies in changes oscillating from reduction of 37% in months of higher water restriction to values exceeding 100% in the months of higher availability, so the use of monthly Q95 instead of the annual Q95 enables the more rational and safe use of water resources.