Market effects on forecasting construction prices using vector error correction models

Construction price forecasting is an essential component to facilitate decision-making for construction contractors, investors and related financial institutions. Construction economists are increasingly interested in seeking a more analytical method to forecast construction prices. Although many studies have focused on construction price modelling and forecasting, few have considered the impacts of large-scale economic events and seasonality. In this study, an advanced multivariate modelling technique, namely the vector correction (VEC) model with dummy variables, was employed. The impacts of global economic events and seasonality are factored into the model to forecast the construction price in the Australian construction market. Research findings suggest that both long-run and dynamic short-term causal relationships exist among the price and levels of supply and demand in the construction market. These relationships drive the construction price and supply and demand, which interact with one another as a loop system. The reliability of forecasting models was examined by the mean absolute percentage error (MAPE) and the Theil's inequality coefficient U tests. The test results suggest that the conventional VEC model and the VEC model with dummy variable are both acceptable for forecasting the construction price, while the VEC model considering external impacts achieves higher prediction accuracy than the conventional VEC model. © 2014 © 2014 Taylor & Francis.

Performance study of a wind-grid system in a semi- arid region and major integration issues

The phenomenal growth in economy experienced in developed countries throughout the 20th century has largely been driven by the availability of conventional energy sources for electricity generation. However, increased concern about fossil fuels and adverse effect of carbon dioxide emission in to atmosphere changed the conventional power system to a viable one by integrating renewable energy sources into the existing system. Among the Renewable Energy (RE) sources, wind energy is one of the fastest growing technologies in reducing the Green House Gas (GHG) emissions in to the atmosphere due to its continuous availability throughout a period. Hence, this paper discusses the performance of a wind-grid connected system in a semi-arid region by conducting a case study. Wilson promontory, one of the best locations for wind generation in Victoria is considered as a case study. Hybrid Optimization Model for Electric Renewable (HOMER) is used as a simulating tool for this analysis. This study also presents the influences of storage system in the proposed Hybrid Power System (HPS) allowing energy to be stored during higher generations or lower load demands. In addition this paper also discusses the major integration issues to facilitate the large scale wind energy into the grid for reliable power generation and distribution.

A panel vector error correction approach to forecasting demand in regional construction markets

Reliable forecasting as to the level of aggregate demand for construction is of vital importance to developers, builders and policymakers. Previous construction demand forecasting studies mainly focused on temporal estimating using national aggregate data. The construction market can be better represented by a group of interconnected regions or local markets rather than a national aggregate, and yet regional forecasting techniques have rarely been applied. Furthermore, limited research has applied regional variations in construction markets to construction demand modelling and forecasting. A new comprehensive method is used, a panel vector error correction approach, to forecast regional construction demand using Australia’s state-level data. The links between regional construction demand and general economic indicators are investigated by panel cointegration and causality analysis. The empirical results suggest that both long-run and causal links are found between regional construction demand and construction price, state income, population, unemployment rates and interest rates. The panel vector error correction model can provide reliable and robust forecasting with less than 10% of the mean absolute percentage error for a medium-term trend of regional construction demand and outperforms the conventional forecasting models (panel multiple regression and time series multiple regression model). The key macroeconomic factors of construction demand variations across regions in Australia are also presented. The findings and robust econometric techniques used are valuable to construction economists in examining future construction markets at a regional level.

Power, effects, confidence, and significance: an investigation of statistical practices in nursing research

Objectives: To (a) assess the statistical power of nursing research to detect small, medium, and large effect sizes; (b) estimate the experiment-wise Type I error rate in these studies; and (c) assess the extent to which (i) a priori power analyses, (ii) effect sizes (and interpretations thereof), and (iii) confidence intervals were reported. Design: Statistical review. Data sources: Papers published in the 2011 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. Review methods: Papers were assessed for statistical power, control of experiment-wise Type I error, reporting of a priori power analyses, reporting and interpretation of effect sizes, and reporting of confidence intervals. The analyses were based on 333 papers, from which 10,337 inferential statistics were identified. Results: The median power to detect small, medium, and large effect sizes was .40 (interquartile range [. IQR]. = .24-.71), .98 (IQR= .85-1.00), and 1.00 (IQR= 1.00-1.00), respectively. The median experiment-wise Type I error rate was .54 (IQR= .26-.80). A priori power analyses were reported in 28% of papers. Effect sizes were routinely reported for Spearman's rank correlations (100% of papers in which this test was used), Poisson regressions (100%), odds ratios (100%), Kendall's tau correlations (100%), Pearson's correlations (99%), logistic regressions (98%), structural equation modelling/confirmatory factor analyses/path analyses (97%), and linear regressions (83%), but were reported less often for two-proportion z tests (50%), analyses of variance/analyses of covariance/multivariate analyses of variance (18%), t tests (8%), Wilcoxon's tests (8%), Chi-squared tests (8%), and Fisher's exact tests (7%), and not reported for sign tests, Friedman's tests, McNemar's tests, multi-level models, and Kruskal-Wallis tests. Effect sizes were infrequently interpreted. Confidence intervals were reported in 28% of papers. Conclusion: The use, reporting, and interpretation of inferential statistics in nursing research need substantial improvement. Most importantly, researchers should abandon the misleading practice of interpreting the results from inferential tests based solely on whether they are statistically significant (or not) and, instead, focus on reporting and interpreting effect sizes, confidence intervals, and significance levels. Nursing researchers also need to conduct and report a priori power analyses, and to address the issue of Type I experiment-wise error inflation in their studies. © 2013 .

A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.

Migrating swans profit from favourable changes in wind conditions at low altitude

Because energy reserves limit flight range, wind assistance may be of crucial importance for migratory birds. We tracked eight Bewick's swans Cygnus columbianus bewickii, using 95-g satellite transmitters with altimeters and activity sensors, during their spring migration from Denmark to northern Russia in 1996. During the 82 occasions where a swan's location was recorded in flight, average flight altitude was 165 m a.s.1. with a maximum of 759 m a.s.1., despite winds often being more favourable at higher altitudes. We also counted Bewick's swans departing from the Gulf of Finland and subsequently passing an observatory in the next major stop-over area 800 km further north in the White Sea, northern Russia, during the springs of 1994, 1995 and 1996. A comparison of these counts with wind data provided evidence for Bewick's swans using favourable changes in wind conditions to embark on migration. Changes in the numbers of birds arriving in the White Sea correlated best with favourable changes in winds in the Gulf of Finland 1 day earlier. Again, migratory volume showed a correlation with winds at low altitudes only, despite wind conditions for the swans being more favourable at high altitudes. We conclude that the relatively large Bewick's swan tends to gear its migration to wind conditions at low altitude only. We argue that Bewick's swans do not climb to high altitudes because of mechanical and physiological limitations with respect to the generation of power for flight and to avoid rapid dehydration.

Flight costs and fuel composition of a bird migrating in a wind tunnel

We studied the energy and protein balance of a Thrush Nightingale Luscinia luscinia, a small long-distance migrant, during repeated 12-hr long flights in a wind tunnel and during subsequent two-day fueling periods. From the energy budgets we estimated the power requirements for migratory flight in this 26 g bird at 1.91 Watts. This is low compared to flight cost estimates in birds of similar mass and with similar wing shape. This suggests that power requirements for migratory flight are lower than the power requirements for nonmigratory flight. From excreta production during flight, and nitrogen and energy balance during subsequent fueling, the dry protein proportion of stores was estimated to be around 10%. A net catabolism of protein during migratory flight along with that of fat may reflect a physiologically inevitable process, a means of providing extra water to counteract dehydration, a production of uric acid for anti-oxidative purposes, and adaptive changes in the size of flight muscles and digestive organs in the exercising animal.

Energy expenditure in relation to flight speed: what is the power of mass loss rate estimates?

The relationship between mass loss rate and chemical power in flying birds is analysed with regard to water and heat balance. Two models are presented: the first model is applicable to situations where heat loads are moderate. i.e. when heat balance can be achieved by regulating non-evaporative heat loss, and evaporative water loss is minimised. The second model is applicable when heat loads are high, non-evaporative heat loss is maximised. and heat balance has to be achieved by regulating evaporative heat loss. The rates of mass loss of two Thrush Nightingales Luscinia luscinia and one Teal Anas crecca were measured at various flight speeds in a wind tunnel. Estimates of metabolic water production indicate that the Thrush Nightingales did not dehydrate during experimental flights. Probably, the Thrush Nightingales maintained heat balance without actively increasing evaporative cooling. The Teal, however, most likely had to resort to evaporative cooling, although it may not have dehydrated. Chemical power was estimated from our mass loss rate data using the minimum evaporation model for the Thrush Nightingales and the evaporative heat regulation model for the Teal. For both Thrush Nightingales and the Teal, the chemical power calculated from our mass loss rate data showed a greater change with speed (more 'U-shaped' curve) than the theoretically predicted chemical power curves based on aerodynamic theory. The minimum power speeds calculated from our data differed little from theoretical predictions but maximum range speeds were drastically different. Mass loss rate could potentially be used to estimate chemical power in flying birds under laboratory conditions where temperature and humidity are controlled. However, the assumptions made in the models and the model predictions need further testing.

Wingbeat frequency and the body drag anomaly: wind-tunnel observations on a thrush nightingale (Luscinia Luscinia) and a teal (Anas crecca)

A teal (Anas crecca) and a thrush nightingale (Luscinia luscinia) were trained to fly in the Lund wind tunnel for periods of up to 3 and 16 h respectively. Both birds flew in steady flapping flight, with such regularity that their wingbeat frequencies could be determined by viewing them through a shutter stroboscope. When flying at a constant air speed, the teal's wingbeat frequency varied with the 0.364 power of the body mass and the thrush nightingale's varied with the 0.430 power. Both exponents differed from zero, but neither differed from the predicted value (0.5) at the 1 % level of significance. The teal continued to flap steadily as the tunnel tilt angle was varied from -1° (climb) to +6° (descent), while the wingbeat frequency declined progressively by about 11%. In both birds, the plot of wingbeat frequency against air speed in level flight was U-shaped, with small but statistically significant curvature. We identified the minima of these curves with the minimum power speed (Vmp) and found that the values predicted for Vmp, using previously published default values for the required variables, were only about two-thirds of the observed minimum-frequency speeds. The discrepancy could be resolved if the body drag coefficients (CDb) of both birds were near 0.08, rather than near 0.40 as previously assumed. The previously published high values for body drag coefficients were derived from wind-tunnel measurements on frozen bird bodies, from which the wings had been removed, and had long been regarded as anomalous, as values below 0.01 are given in the engineering literature for streamlined bodies. We suggest that birds of any size that have well-streamlined bodies can achieve minimum body drag coefficients of around 0.05 if the feet can be fully retracted under the flank feathers. In such birds, field observations of flight speeds may need to be reinterpreted in the light of higher estimates of Vmp. Estimates of the effective lift:drag ratio and range can also be revised upwards. Birds that have large feet or trailing legs may have higher body drag coefficients. The original estimates of around CDb=0.4 could be correct for species, such as pelicans and large herons, that also have prominent heads. We see no evidence for any progressive reduction of body drag coefficient in the Reynolds number range covered by our experiments, that is 21600-215 000 on the basis of body cross-sectional diameter.

Testing for error correction in panel data

This paper proposes new error correction-based cointegration tests for panel data. The limiting distributions of the tests are derived and critical values provided. Our simulation results suggest that the tests have good small-sample properties with small size distortions and high power relative to other popular residual-based panel cointegration tests. In our empirical application, we present evidence suggesting that international healthcare expenditures and GDP are cointegrated once the possibility of an invalid common factor restriction has been accounted for. © 2007 Blackwell Publishing Ltd.

Implementation of fuzzy logic maximum power point tracking controller for photovoltaic system

In this study, simulation and hardware implementation of Fuzzy Logic (FL) Maximum Power Point Tracking (MPPT) used in photovoltaic system with a direct control method are presented. In this control system, no proportional or integral control loop exists and an adaptive FL controller generates the control signals. The designed and integrated system is a contribution of different aspects which includes simulation, design and programming and experimental setup. The resultant system is capable and satisfactory in terms of fastness and dynamic performance. The results also indicate that the control system works without steady-state error and has the ability of tracking MPPs rapid and accurate which is useful for the sudden changes in the atmospheric condition. MATLAB/Simulink software is utilized for simulation and also programming the TMS320F2812 Digital Signal Processor (DSP). The whole system designed and implemented to hardware was tested successfully on a laboratory PV array. The obtained experimental results show the functionality and feasibility of the proposed controller.

Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: a comparative study

The uncertainties of renewable energy have brought great challenges to power system commitment, dispatches and reserve requirement. This paper presents a comparative study on integration of renewable generation uncertainties into SCUC (stochastic security-constrained unit commitment) considering reserve and risk. Renewable forecast uncertainties are captured by a list of PIs (prediction intervals). A new scenario generation method is proposed to generate scenarios from these PIs. Different system uncertainties are considered as scenarios in the stochastic SCUC problem formulation. Two comparative simulations with single (E1: wind only) and multiple sources of uncertainty (E2: load, wind, solar and generation outages) are investigated. Five deterministic and four stochastic case studies are performed. Different generation costs, reserve strategies and associated risks are compared under various scenarios. Demonstrated results indicate the overall costs of E2 is lower than E1 due to penetration of solar power and the associated risk in deterministic cases of E2 is higher than E1. It implies the superimposed effect of uncertainties during uncertainty integration. The results also demonstrate that power systems run a higher level of risk during peak load hours, and that stochastic models are more robust than deterministic ones.

Optimal coordination of G2V and V2G to support power grids with high penetration of renewable energy

Electric vehicles (EVs) have recently gained much popularity as a green alternative to fossil-fuel cars and a feasible solution to reduce air pollution in big cities. The use of EVs can also be extended as a demand response tool to support high penetration of renewable energy (RE) sources in future smart grid. Based on the certainty equivalent adaptive control (CECA) principle and a customer participation program, this paper presents a novel control strategy using optimization technique to coordinate not only the charging but also the discharging of EV batteries to deal with the intermittency in RE production. In addition, customer charging requirements and schedules are incorporated into the optimization algorithm to ensure customer satisfaction, and further improve the control performance. The merits of this scheme are its simplicity, efficiency, robustness and readiness for practical applications. The effectiveness of the proposed control algorithm is demonstrated by computer simulations of a power system with high level of wind energy integration.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular it delivers a zero-limiting mean-squared error if the number of forecasts and the number of post-sample time periods is sufficiently large. We also develop a zero-mean test for the average bias. Monte-Carlo simulations are conducted to evaluate the performance of this new technique in finite samples. An empirical exercise, based upon data from well known surveys is also presented. Overall, these results show promise for the bias-corrected average forecast.