Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance

Intermediate Layers for Laser-Crystallised Thin-Film Silicon Solar Cells on Glass

Liquid phase crystallised silicon on glass (LPCSG) is a promising solar cell technology using high-quality polycrystalline silicon thin films to produce low-cost solar modules. In this work, a line-focused continuous-wave diode laser is used to melt and recrystallise 10 micron thick silicon films on borosilicate glass. Different intermediate layers between the glass and the silicon are assessed for their suitability for such cells. SiOx, SiNx and SiCx are tested either as single films or in multilayer stacks with regard to optical properties as an anti-reflection coating (ARC), wettability, silicon crystal quality, diffusion properties and device performance.SiOx is found to be optically indistinguishable from the glass, providing no anti-reflective benefit. SiCx is limited to thin (~20 nm) films due to parasitic absorbance. Combining thin SiCx with a thin Si-side SiOx layer creates an acceptable ARC. SiNx provides the best ARC.SiCx layers, either alone or in stacks with SiOx, are found to provide the best wettability, allowing the largest range of laser parameters for crystallisation without silicon dewetting. SiOx and SiNx allow a smaller, but sufficient process range. Large crystal grains, several millimetres in length, are formed after crystallisation with all intermediate layer options. The density of twin grain-boundaries is found to be dependent upon intermediate layer class, with single layer SiOx causing the lowest density, followed by SiCx-based layers, then SiNx-based layers.SiOx is found to be the best barrier against boron diffusion from the borosilicate glass, limiting unintentional doping to 7 % for otherwise similar cells. Efficiency up to 11.7 % is measured for a cell with a SiOx/SiNx/SiOx intermediate layer stack. A subsequent degradation is found to reduce this to ~10

Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance

A longitudinal study of the association between dietary factors, serum lipids, and bone marrow lesions of the knee

INTRODUCTION: Bone marrow lesions (BMLs) play an important role in knee osteoarthritis, but their etiology is not well understood. The aim of this longitudinal study was to describe the association between dietary factors, serum lipids, and BMLs. METHODS: In total, 394 older men and women (mean age, 63 years; range, 52 to 79) were measured at baseline and approximately 2.7 years later. BMLs were determined by using T2-weighted fat-saturation magnetic resonance imaging (MRI) by measuring the maximal area of the lesion. Nutrient intake (total energy, fat, carbohydrate, protein, and sugar) and serum lipids were assessed at baseline. RESULTS: Cross-sectionally, dietary factors and lipids were not significantly associated with BMLs. Energy, carbohydrate, and sugar intake (but not fat) were positively associated with a change in BML size (β = 15.44 to 19.27 mm2 per 1 SD increase; all P < 0.05). High-density lipoprotein (HDL) cholesterol tended to be negatively associated with BML change (β = -11.66 mm2 per 1 SD increase; P = 0.088). CONCLUSIONS: Energy, carbohydrate, and sugar intake may be risk factors for BML development and progression. HDL cholesterol seems protective against BMLs. These results suggest that macronutrients and lipids may be important in BML etiology and that dietary modification may alter BML natural history

Cold atoms in space: community workshop summary and proposed road-map

AbstractWe summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies.</jats:p