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.

Interpretation of Section 17 Employment Protection Act in the light of ECJ case law and under consideration of the cases pending before the Federal Labour Court

This article analyses the relevance of the ECJ ruling in Junk for German labour law.

Pro-active protection against racialist discrimination in employment law - lessons from South Africa?

This study investigates the potential of the prohibition of indirect race discrimination to be used for law reform, and to uncover discriminatory practices. It reflects on the history and contents of the concept, and focuses in particular on its application in the Republic of South Africa

HRM and the employment of older workers: Germany and Britain compared

This study examines Human Resource Management (HRM) policies and practices towards older workers in Britain and Germany. While it is widely suggested that older workers have to be better integrated into the labour market, youth-centric HRM is still prevalent. However, HRM is shaped by multiple and contradictory pressures from the international and national institutional environments. We test this dynamic by analysing two national surveys, the German firm panel (IAB)1 and the British Workplace and Employment Relations Survey (WERS).2 Our findings suggest that the institutional environment shapes HR policies and practices distinctively in both countries. We find that age discrimination at the workplace is more prevalent in Germany than in Britain, which can be explained by divergent institutional patterns. As a result, we argue that although both countries will have to continue fostering an age-neutral HR approach, this has to take country-specific institutional peculiarities into account.

Empathy in UK pharmacy students: assessing differences by gender, level in the degree programme, part-time employment and medical status

Background: Empathy is an important aspect of patient–healthcare professional interactions.Aims: To investigate whether gender, level in the degree programme, employment and health status affected empathy scores of undergraduate pharmacy students.Method: All undergraduate pharmacy students (n=529) at Queen’s University Belfast were invited via email to completean online validated empathy questionnaire. Empathy scores were calculated and non-parametric tests used to determine associations between factors.Results: Response rate was 60.1% (318/529) and the mean empathy score was 106.19. Scores can range from 20 to 140,with higher scores representing a greater degree of empathy. There was no significant difference between genders (p=0.211). There was a significant difference in scores across the four levels of the programme (p<0.001); scores were lowest at Level 1 and greatest at Level 4 (final year). There were no significant differences in scores for respondents who had a part-time job, a chronic condition, or took regular medication in comparison to those who did not (p=0.028,p=0.880, p=0.456, respectively).Conclusion: A reasonable level of empathy was found relative to other studies; this could be further enhanced at lower levels of the degree pathway.

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.