Keith A. Ellis

Measuring Energy Efficiency Practices in Mature Data Center: A Maturity Model Approach

Power usage within a Data Center (DC) goes beyond the direct power needs of servers to include networking, cooling, lighting and facilities management. Data centers range from closet-sized operations, drawing a few kilowatts (kW), to mega-sized facilities, consuming tens of megawatts (MWs). In almost all cases, independent of size there exists significant potential to improve both the economic and environmental bottom line of data centers by improve their energy efficiency, however a number of challenges exist. This paper describes the resulting maturity model, which offers a comprehensive value-based method for organizing, evaluating, planning, and improving the energy efficiency of mature data centers. The development process for the maturity model is discussed, detailing the role of design science in its definition.

Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour

Automated behavioural classification and identification through sensors has the potential to improve health and welfare of the animals. Position of a sensor, sampling frequency and window size of segmented signal data has a major impact on classification accuracy in activity recognition and energy needs for the sensor, yet, there are no studies in precision livestock farming that have evaluated the effect of all these factors simultaneously. The aim of this study was to evaluate the effects of position (ear and collar), sampling frequency (8, 16 and 32 Hz) of a triaxial accelerometer and gyroscope sensor and window size (3, 5 and 7 s) on the classification of important behaviours in sheep such as lying, standing and walking. Behaviours were classified using a random forest approach with 44 feature characteristics. The best performance for walking, standing and lying classification in sheep (accuracy 95%, F-score 91%–97%) was obtained using combination of 32 Hz, 7 s and 32 Hz, 5 s for both ear and collar sensors, although, results obtained with 16 Hz and 7 s window were comparable with accuracy of 91%–93% and F-score 88%–95%. Energy efficiency was best at a 7 s window. This suggests that sampling at 16 Hz with 7 s window will offer benefits in a real-time behavioural monitoring system for sheep due to reduced energy needs.

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

Barriers, Challenges, and Recommendations Related to Development of Energy Positive Neighborhoods and Smart Energy Districts

Abstract This chapter presents an overview of the barriers to smart, demand-side interventions for neighborhoods and districts. Barriers are classified as political/regulatory, economic, social, and technological. Subsequently, drawing on experience from the COOPERaTE project, the specific challenges for the implementation of the Energy Positive Neighborhood (EPN) concept are reviewed. These challenges are presented in classifications of: energy modeling and simulation, performance assessment, information technology, and business model development. Finally, recommendations to aid implementation of the EPN concept and, more generally, smart district and demand-side interventions are detailed.