A refined parametric model for short term load forecasting

Abstract We present a refined parametric model for forecasting electricity demand which performed particularly well in the recent Global Energy Forecasting Competition (GEFCom 2012). We begin by motivating and presenting a simple parametric model, treating the electricity demand as a function of the temperature and day of the data. We then set out a series of refinements of the model, explaining the rationale for each, and using the competition scores to demonstrate that each successive refinement step increases the accuracy of the model’s predictions. These refinements include combining models from multiple weather stations, removing outliers from the historical data, and special treatments of public holidays

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested

In the mood: the dynamics of collective sentiments on Twitter

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source Sentistrength program. Specifically we make three contributions. Firstly, we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example, they use positive sentiment more often and negative sentiment less often. Secondly, we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable with those obtained from our empirical dataset

Can a native rodent species limit the invasive potential of a non-native rodent species in tropical agroforest habitats?

BACKGROUND Little is known about native and non-native rodent species interactions in complex tropical agro-ecosystems. We hypothesised that the native non-pest rodent Rattus everetti may be competitively dominant over the invasive pest rodent Rattus tanezumi within agroforests. We tested this experimentally by using pulse removal for three consecutive months to reduce populations of R. everetti in agroforest habitat and assessed over 6-months the response of R. tanezumi and other rodent species. RESULTS Following removal, R. everetti individuals rapidly immigrated into removal sites. At the end of the study period, R. tanezumi were larger and there was a significant shift in their microhabitat use with respect to the use of ground vegetation cover following the perturbation of R. everetti. Irrespective of treatment, R. tanezumi selected microhabitat with less tree canopy cover, indicative of severely disturbed habitat, whereas, R. everetti selected microhabitat with a dense canopy. CONCLUSION Our results suggest that sustained habitat disturbance in agroforests favours R. tanezumi, whilst the regeneration of agroforests towards a more natural state would favour native species and may reduce pest pressure in adjacent crops. In addition, the rapid recolonisation of R. everetti suggests this species would be able to recover from non-target impacts of short-term rodent pest control

A genetic algorithm approach for modelling low voltage network demands

Distribution network operators (DNOs) are increasingly concerned about the impact of low carbon technologies on the low voltage (LV) networks. More advanced metering infrastructures provide numerous opportunities for more accurate load flow analysis of the LV networks. However, such data may not be readily available for DNOs and in any case is likely to be expensive. Modelling tools are required which can provide realistic, yet accurate, load profiles as input for a network modelling tool, without needing access to large amounts of monitored customer data. In this paper we outline some simple methods for accurately modelling a large number of unmonitored residential customers at the LV level. We do this by a process we call buddying, which models unmonitored customers by assigning them load profiles from a limited sample of monitored customers who have smart meters. Hence the presented method requires access to only a relatively small amount of domestic customers' data. The method is efficiently optimised using a genetic algorithm to minimise a weighted cost function between matching the substation data and the individual mean daily demands. Hence we can show the effectiveness of substation monitoring in LV network modelling. Using real LV network modelling, we show that our methods perform significantly better than a comparative Monte Carlo approach, and provide a description of the peak demand behaviour

A Peak Reduction Scheduling Algorithm for Storage Devices on the Low Voltage Network

Reinforcing the Low Voltage (LV) distribution network will become essential to ensure it remains within its operating constraints as demand on the network increases. The deployment of energy storage in the distribution network provides an alternative to conventional reinforcement. This paper presents a control methodology for energy storage to reduce peak demand in a distribution network based on day-ahead demand forecasts and historical demand data. The control methodology pre-processes the forecast data prior to a planning phase to build in resilience to the inevitable errors between the forecasted and actual demand. The algorithm uses no real time adjustment so has an economical advantage over traditional storage control algorithms. Results show that peak demand on a single phase of a feeder can be reduced even when there are differences between the forecasted and the actual demand. In particular, results are presented that demonstrate when the algorithm is applied to a large number of single phase demand aggregations that it is possible to identify which of these aggregations are the most suitable candidates for the control methodology

Preparation For Fatherhood: A Role For Olfactory Communication During Human Pregnancy?

There is evidence across a range of bi-parental species that physiological changes may occur in partnered males prior to the birth of an infant. It has been hypothesised that these hormonal changes might facilitate care-giving behaviours, which could augment infant survival. The mechanism that induces these changes has not been identified, but evidence from several species suggests that odour may play a role. The current study investigated this in humans by recording testosterone and psychological measures related to infant interest and care in men (n=91) both before and after exposure to odours from either pregnant women or non-pregnant control women. We found no evidence for an effect of odour cues of pregnancy on psychological measures including self-reported sociosexual orientation and social dominance scores, ratings of infant or adult faces, or testosterone levels. However, we found that brief exposure to post-partum odours significantly increased the reward value of infant faces. Our study is the first to show that the odour of peri-partum women may lead to upregulation of men’s interest in infants

Targeted metagenomics of active microbial populations with stable-isotope probing

The ability to explore microbial diversity and function has been enhanced by novel experimental and computational tools. The incorporation of stable isotopes into microbial biomass enables the recovery of labeled nucleic acids from active microorganisms, despite their initial abundance and culturability. Combining stable-isotope probing (SIP) with metagenomics provides access to genomes from microorganisms involved in metabolic processes of interest. Studies using metagenomic analysis on DNA obtained from DNA-SIP incubations can be ideal for the recovery of novel enzymes for biotechnology applications, including biodegradation, biotransformation, and biosynthesis. This chapter introduces metagenomic and DNA-SIP methodologies, highlights biotechnology-focused studies that combine these approaches, and provides perspectives on future uses of these methods as analysis tools for applied and environmental microbiology

A new error measure for forecasts of household-level, high resolution electrical energy consumption

As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the single household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the appropriate charging and discharging cycles. However, before such methods can be developed, validation measures are required which can assess the accuracy and usefulness of forecasts of volatile and noisy household-level demand. In this paper we introduce a new forecast verification error measure that reduces the so called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as Mean Absolute Error and p-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters and discuss the effect of the permutation restriction

The conserved C-terminus of the PcrA/UvrD helicase interacts directly with RNA polymerase

Copyright: © 2013 Gwynn et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by a Wellcome Trust project grant to MD (Reference: 077368), an ERC starting grant to MD (Acronym: SM-DNA-REPAIR) and a BBSRC project grant to PM, NS and MD (Reference: BB/I003142/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD