Assessing the Impact of Technology on Aircraft Systems using Technology Impact Forecasting

In today’s atmosphere of constrained defense spending and reduced research budgets, determining how to allocate resources for research and design has become a critical and challenging task. In the area of aircraft design there are many promising technologies to be explored, yet limited funds with which to explore them. In addition, issues concerning uncertainty in technology readiness as well as the quantification of the impact of a technology (or combinations of technologies), are of key importance during the design process. This paper presents a methodology that details a comprehensive and structured process in which to quantitatively explore the effects of technology for a given baseline aircraft. This process, called Technology Impact Forecasting (TIF), involves the creation of a assessment environment for use in conjunction with defined technology scenarios, and will have a significant impact on resource allocation strategies for defense acquisition. The advantages and limitations of the method are discussed. In addition, an example TIF application, that of an Uninhabited Combat Aerial Vehicle, is presented and serves to illustrate the applicability of this methodology to a military system.

Offshore wind power forecasting error and electricity market implications

Wind energy has been identified as key to the European Union’s 2050 low carbon economy. However, as wind is a variable resource and stochastic by nature, it is difficult to plan and schedule the power system under varying wind power generation. This paper investigates the impacts of offshore wind power forecast error on the operation and management of a pool-based electricity market in 2050. The impact of the magnitude and variance of the offshore wind power forecast error on system generation costs, emission costs, dispatch-down of wind, number of start-ups and system marginal price is analysed. The main findings of this research are that the magnitude of the offshore wind power forecast error has the largest impact on system generation costs and dispatch-down of wind, but the variance of the offshore wind power forecast error has the biggest impact on emissions costs and system marginal price. Overall offshore wind power forecast error variance results in a system marginal price increase of 9.6% in 2050.

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality-forecasting models be associated with real-world trends in health-related variables? Does inclusion of health-related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle-related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models.

Bandwidth selection by cross-validation for forecasting long memory financial time series

The paper addresses the issue of choice of bandwidth in the application of semiparametric estimation of the long memory parameter in a univariate time series process. The focus is on the properties of forecasts from the long memory model. A variety of cross-validation methods based on out of sample forecasting properties are proposed. These procedures are used for the choice of bandwidth and subsequent model selection. Simulation evidence is presented that demonstrates the advantage of the proposed new methodology.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

A methodology to analyse the impact of offshore wind forecasting error on electricity markets

Currently wind power is dominated by onshore wind farms in the British Isles, but both the United Kingdom and the Republic of Ireland have high renewable energy targets, expected to come mostly from wind power. However, as the demand for wind power grows to ensure security of energy supply, as a potentially cheaper alternative to fossil fuels and to meet greenhouse gas emissions reduction targets offshore wind power will grow rapidly as the availability of suitable onshore sites decrease. However, wind is variable and stochastic by nature and thus difficult to schedule. In order to plan for these uncertainties market operators use wind forecasting tools, reserve plant and ancillary service agreements. Onshore wind power forecasting techniques have improved dramatically and continue to advance, but offshore wind power forecasting is more difficult due to limited datasets and knowledge. So as the amount of offshore wind power increases in the British Isles robust forecasting and planning techniques are even more critical. This paper presents a methodology to investigate the impacts of better offshore wind forecasting on the operation and management of the single wholesale electricity market in the Republic of Ireland and Northern Ireland using PLEXOS for Power Systems. © 2013 IEEE.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

Electric vehicle capacity forecasting model with application to load levelling

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

Forecasting invasions: resource use by mussels informs invasion patterns along the South African coast

Invasive species are often more able to rapidly and efficiently utilise resources than natives, and comparing per capita resource use at different resource densities among invaders and trophically analogous natives could allow for reliable predictions of invasiveness. In South Africa, invasion by the Mediterranean mussel Mytilus galloprovincialis has transformed wave-exposed shores, negatively affecting native mussel species. Currently, South Africa is experiencing a second mussel invasion with the recent detection of the South American Semimytilus algosus. We tested per capita uptake of an algal resource by invading M. galloprovincialis, S. algosus, and the native Aulacomya atra at different algal concentrations and temperatures, representing the west and south coasts of South Africa, to examine whether their per capita resource use could be a predictor of their spread and subsequent invasiveness. Regardless of temperature, M. galloprovincialis was the most efficient consumer, significantly reducing algal cells compared to the other species when the resource was presented in both low and high starting densities. Furthermore, these findings aligned with a greater biomass of M. galloprovincialis on the shore in comparison with the other species. Resource use by the new invader S. algosus was dependent on the density of resource and, although this species was efficient at low algal concentrations at cooler temperatures, this pattern broke down at higher algal densities. This was once more reflected in lower biomass in surveys of this species along the cool west coast. We therefore forecast that S. algosus will be become established along the south coast; however, we also predict that M. galloprovincialis will maintain dominance on these shores.

The Baltic Dry Index: cyclicalities, forecasting and hedging strategies

The cyclical properties of the Baltic Dry Index (BDI) and their implications for forecasting performance are investigated. We find that changes in the BDI can lead to permanent shocks to trade of major exporting economies. In our forecasting exercise, we show that commodities and trigonometric regression can lead to improved predictions and then use our forecasting results to perform an investment exercise and to show how they can be used for improved risk management in the freight sector.

Assessing the combined effect of land management and wildfire on runoff and soil erosion in north central Portugal

Strong and sometimes extreme responses in runoff and soil erosion following wildfires have been reported worldwide. However, in the case of North-Central Portugal, little research had been carried out regarding the hydrologic and erosive impacts of several land management activities in recently burnt areas (such as ground preparation, post-fire logging or post-fire mitigation treatments). This study aims to assess post-fire runoff and soil erosion response on Eucalypt and Maritime pine plantations during the first, second and third years following wildfires. The effect of several pre-fire ground preparation operations (ploughed down-slope, contour ploughed and inclined terraces), post-fire logging activities (on both the eucalypt and pine plantations), as well as the application of hydromulch (a post-fire emergency treatment) on overland flow and soil erosion were compared to burnt but undisturbed and untreated areas. The intensive monitoring of runoff, soil erosion and selected soil properties served to determine the main factors involved in post-fire runoff and soil erosion and their spatial and temporal variation. Soil water repellency deserved special attention, due to its supposed important role for overland flow generation. Repeated rainfall simulation experiments (RSE’s), micro-scale runoff plots and bounded sediment fences were carried out and/or installed immediately after the wildfire on seven burnt slopes. Micro-scale runoff plots results under natural rainfall conditions were also compared to the RSE’s results, which was useful for assessing the representativeness of the data obtained with artificial rainfall. The results showed comparable runoff coefficient (20-60%) but lower sediment losses (125-1000 g m-2) than prior studies in Portugal, but especially outside Portugal. Lower sediment losses were related with the historic intensive land use in the area. In evaluating these losses, however, the shallowness and stoniness of the soils, as well as the high organic matter fraction of the eroded sediments (50%) must not be overlooked. Sediment limited erosion was measured in all the ploughed sites, probably due to the time since ploughing (several years). The disturbance of the soil surface cover due to post-fire logging and wood extraction substantially increased sediment losses at both the pine and eucalypt sites. Hydromulch effectiveness in reducing the runoff (70%) and sediment losses (83%) was attributed to the protective high coverage provided by hydromulch. The hydromulch significantly affected the soil cover and other soil properties and these changes also reduced the soil erosion risk. The rainfall amount was the main factor explaining the variance in runoff. However, a shift from rainfall amount to rainfall intensity was detected when either the surface cover or the infiltration capacity (hydrophilic conditions) increased. Sediment losses were controlled by rainfall intensity and surface cover. The role of soil water repellency on runoff generation was not consistent; the overall repellency levels alone were not enough to assess its hydrological impact. Soil water repellency explained runoff generation in the specific-sites model better than in the overall model. Additionally, soil moisture content was a better predictor for soil water repellency than antecedent rainfall. The natural rainfall results confirmed that RSE’s were able to capture the specific sediment losses and its organic matter content as well as the differences between the ploughed and unploughed sites. Repeated RSE’s also captured the seasonal variations in runoff and sediment losses attributed to soil water repellency. These results have implications for post-fire soil erosion modelling and soil conservation practices in the region, or areas with the same land use, climate and soil characteristics. The measured sediment loss, as well as the increasing frequency of ploughing in recently burnt and unburnt eucalypt stands, suggests ploughing is not an effective as a soil conservation measure. Logging activities with less impact are recommended in order to maintain the forest litter protecting the soil surface. Due to its high effectiveness in reducing runoff and soil erosion, hydromulch is recommended for highly sensitive and vulnerable areas.