An optimized mean variance estimation method for uncertainty quantification of wind power forecasts

A statistical optimized technique for rapid development of reliable prediction intervals (PIs) is presented in this study. The mean-variance estimation (MVE) technique is employed here for quantification of uncertainties related with wind power predictions. In this method, two separate neural network models are used for estimation of wind power generation and its variance. A novel PI-based training algorithm is also presented to enhance the performance of the MVE method and improve the quality of PIs. For an in-depth analysis, comprehensive experiments are conducted with seasonal datasets taken from three geographically dispersed wind farms in Australia. Five confidence levels of PIs are between 50% and 90%. Obtained results show while both traditional and optimized PIs are hypothetically valid, the optimized PIs are much more informative than the traditional MVE PIs. The informativeness of these PIs paves the way for their application in trouble-free operation and smooth integration of wind farms into energy systems. © 2014 Elsevier Ltd. All rights reserved.

Aggregation of Pi-based forecast to enhance prediction accuracy

In contrast to point forecast, prediction interval-based neural network offers itself as an effective tool to quantify the uncertainty and disturbances that associated with process data. However, single best neural network (NN) does not always guarantee to predict better quality of forecast for different data sets or a whole range of data set. Literature reported that ensemble of NNs using forecast combination produces stable and consistence forecast than single best NN. In this work, a NNs ensemble procedure is introduced to construct better quality of Pis. Weighted averaging forecasts combination mechanism is employed to combine the Pi-based forecast. As the key contribution of this paper, a new Pi-based cost function is proposed to optimize the individual weights for NN in combination process. An optimization algorithm, named simulated annealing (SA) is used to minimize the PI-based cost function. Finally, the proposed method is examined in two different case studies and compared the results with the individual best NNs and available simple averaging Pis aggregating method. Simulation results demonstrated that the proposed method improved the quality of Pis than individual best NNs and simple averaging ensemble method.

Demo abstract: wind measurements for water quality studies in urban reservoirs

Water quality monitoring and prediction are critical for ensuring the sustainability of water resources which are essential for social security, especially for countries with limited land like Singapore. For example, the Singapore government identified water as a new growth sector and committed in 2006 to invest S$ 330 million over the following five years for water research and development [1]. To investigate the water quality evolution numerically, some key water quality parameters at several discrete locations in the reservoir (e.g., dissolved oxygen, chlorophyll, and temperature) and some environmental parameters (e.g., the wind distribution above water surface, air temperature and precipitation) are used as inputs to a three-dimensional hydrodynamics-ecological model, Estuary Lake and Coastal Ocean Model - Computational Aquatic Ecosystem Dynamics Model (ELCOM-CAEDYM) [2]. Based on the calculation in the model, we can obtain the distribution of water quality in the whole reservoir. We can also study the effect of different environmental parameters on the water quality evolution, and finally predict the water quality of the reservoir with a time step of 30 seconds. In this demo, we introduce our data collection system which enables water quality studies with real-time sensor data.

Giving solidity to pure wind: temporising as transformation

Jared Diamond asked the acclaimed evolutionary biologist Ernst Mayr (1904-2005) why Aristotle didn’t come up with the theory of evolution. Mayr’s answer was ‘Frage stellen’ which Diamond translates as ‘a way of asking questions [sic]’ (Byrne 2013). The idea that a particular way-of-asking might generate a particular way-of-knowing and, indeed, a particular branch-of-knowledge, is utterly intriguing, especially when we frame the practice of creative writing in those terms: as a way of asking questions.Drusilla Modjeska unpacks the concept of ‘temporising’ in her article ‘Writing Poppy’ (Modjeska 2002: 75). This discussion invites us to consider the generative capabilities of the temporising space – as an imaginative space for writers, as an alternate way of asking questions … of seeing, being, knowing. In narrative, the questions that underpin the work do not necessarily appear in the surface-content of the text. In this way, the story is a metaphorical representation of the questions that lie beneath. As Aristotle suggests, metaphor relies on ‘an intuitive perception of the similarity [to homoion theorein] in dissimilars’ (Ricoeur 1977: 23). In narrative we contemplate a question, or an idea, within the context of a metaphorical other. This is a form of temporising: of ‘slip[ping] into other time frames’ as a means of ‘retreat[ing] and consider[ing]’ (Modjeska 2002: 75, 76). In narrative time, we consider one thing through an alternate temporal lens. We prevaricate in otherness.Fiction-making represents a very particular way of asking questions. With reference to the process of writing the short story – ‘Everything that matters is silvery white’ – it is clear that ‘making’ narrative is a way of asking questions that is assisted by the transformative temporising space.

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.

Sowing the wind

'Sowing the wind' is a chapter from the novel manuscript 'The Earth does not get fat'

Sensor placement and measurement of wind for water quality studies in urban reservoirs

We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches.

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.

Skipping swans: fuelling rates and wind conditions determine differential use of migratory stopover sites of Bewick's Swans Cygnus bewickii

Some migratory birds refuel at stopover sites that they by-pass on the return trip. In theory, this skipping behaviour is only expected in time-selected migrants when the overflown site is of a lower quality than the departure site. We provide empirical evidence that quality differences in stopover sites are the cause for skipping in Bewick's Swans Cygnus bewickii tracked by satellite telemetry. Two and five complete tracks were recorded in spring and autunm, respectively, showing that the White Sea was visited for c. 2 weeks in spring, but by-passed (or visited for a few days at the most) in autumn. Skipping of the White Sea in autumn was predicted by a dynamic programming model which was based on calculated gain rates during stopover in the Pechora Delta and the White Sea. This prediction was not sensitive to plausible variations in gain rates. Relative to the Pechora Delta the White Sea is a poor site because a large tidal amplitude precludes foraging on the beds of the submerged macrophyte Fennel Pondweed Potamogeton pectinatus during high tide. The dynamic programming model predicted a fast autunm migration. However, the phenology of autunm arrival dates of Bewick's Swans on the wintering grounds revealed that only in three out of ten years a significant number of birds was able to reach the wintering grounds without refuelling. In the other years, unfavourable wind conditions along the Russian/Baltic part of the route prevented such non-stop migration.

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.

Variation in energy intake and basal metabolic rate of a bird migrating in a wind tunnel

1. We studied the changes in body mass, metabolizable energy intake rate (ME) and basal metabolic rate (BMR) of a Thrush Nightingale, Luscinia luscinia, following repeated 12-h migratory flights in a wind tunnel. In total the bird flew for 176 h corresponding to 6300 km. This is the first study where the fuelling phase has been investigated in a bird migrating in captivity.

2. ME was very high, supporting earlier findings that migrating birds have among the highest intake rates known among homeotherms. ME was significantly higher the second day of fuelling, indicating a build-up of the capacity of the digestive tract during the first day of fuelling.

3. Further indications of an increase in size or activity level of metabolically active structures during fuelling come from the short-term variation in BMR, which increased over the 2-day fuelling period with more than 20%, and in almost direct proportion to body mass. However, mass-specific BMR decreased over the season.

4. The patterns of mass change, ME and BMR of our focal bird following two occasions of 12-h fasts were the same as after flights, indicating that fast and flight may involve similar physiological processes.

5. The relatively low ME the first day following a flight may be a contributing factor to the well-known pattern that migrating birds during stopover normally lose mass the first day of fuelling.