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Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

This dataset consists of hourly internal and daily external temperature data from 82 certified Passivhaus dwellings in the UK. The data can be used for calculating overheating risk and guaging how comfortable a home would be in the summer. This data come from 16 different sites and includes houses and flats. Some of the data is from the living room only, for other dwellings there were sensors in muitple rooms and these are indicated. As this data was compared to CIBSE TM59 "Design methodology for the assessment of overheating risk in homes", there is a calculation of the running mean temperature and maximum temperature. The variables are Timestamp = time and date SiteID = Site number (1-16) DWType = dwelling type (House or Flat) HouseID = unique reference number for each dwelling in dataset Room = room type LR = living room , BR= bedroom, KI= Kitchen, BT= bathroom T.int = internal temperature (mean hourly) T.ext.daily = external temperature (mean daily) T.rm = running mean temperature calculated using the method described in CIBSE TM59 T.max = maximum daily intenral temperature calculated using the method described in CIBSE TM59 This data was provided by the Technology Stratergy Board Building Performance Evaluation Program, and is available from the digital catapault. Other data was provided by WARM low energy Consultancy and indidiual home owners. All data has been anonymised

QTDIAN - Quantification of Technological DIffusion and sociAl constraiNts - is a toolbox of qualitative and quantitative descriptions of socio-technical and political aspects of the energy transition that influence the overall potential, the rate of energy-related technology and service diffusion and the design of the future energy system. The output of QTIDIAN is empirically founded datasets of social and political drivers and barriers of the transition, both in the form of raw data describing past and current developments and manipulated to constitute consistent quantifications of the storylines. Here you can download the data for six QTDIAN themes: Socially feasible scaling of energy technologies Policy preferences & dynamics Barriers to infrastructural development (wind energy, grid development) Citizen energy Private energy demand Further information on the QTDIAN modelling toolbox and the data can be found in the SENTINEL Deliverable 2.3 and Deliverable 2.4: S��sser, D., al Rakouki, H., & Lilliestam, J.(2021). The QTDIAN modelling toolbox���Quantification of social drivers and constraints of the diffusion of energy technologies. Deliverable 2.3. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS). S��sser, D., Pickering, B., Chatterjee, S., Oreggioni, G., Stavrakas, V., & Lilliestam, J.(2021). Integration of socio-technological transition constraints into energy demand and systems models. Deliverable 2.5. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS).

This repository consists of two databases- CASE-ONSHORE and CASE-OFFSHORE, generated using OpenFAST v2.4 on NREL's 10-MW reference wind turbine for training data-driven probabilistic load surrogate models. The data is to be used for mapping 10-minute average environmental conditions to the corresponding 10-minute load statistics such as load average, fatigue and range at various locations on the tower and blades.

The repository contains the underlining data for "On the wake deflection of vertical axis wind turbines by pitched blades" This repository consists of experimental (PIV and force measurements) data in the wake of VAWTs with different blade pitch angles. The measured cases include an isolated VAWT with -10, 0, 10 degree pitch, respectively. Author contribution: Ming Huang: Conceptualization; Methodology; Validation; Experimental design; Carrying out the experiment; postprocessing Yugandhar Vijaykumar Patil: Design assistant; Carrying out the experiment; postprocessing Andrea Sciacchitano: Conceptualization; Experimental design; Methodology; Validation; Delphine De Tavernier: Conceptualization Carlos Simao Ferreira: Conceptualization; Methodology; Validation; Experimental design

The Intergovernmental Panel on Climate Change (IPCC) report that to limit warming to 1.5 °C, Bioenergy with Carbon Capture and Storage (BECCS) is required. Integrated assessment models (IAMS) predict that a land area between the size of Argentina and Australia is required for bioenergy crops, a 3–7 time increase in the current bioenergy planting area globally. The authors pose the question of whether vertical farming (VF) technology can enable BECCS deployment, either via land sparing or supply. VF involves indoor controlled environment cultivation, and can increase productivity per unit land area by 5–10 times. VF is predominantly being used to grow small, high value leafy greens with rapid growth cycles. Capital expenditure, operational expenditure, and sustainability are challenges in current VF industries, and will affect the ability to utilise this technology for other crops. The authors argue that, whilst challenging, VF could help reach wider climate goals. Application of VF for bioenergy crops could be a game changer in delivering BECCS technologies and may reduce the land footprint required as well as the subsequent associated negative environmental impacts. VF bioenergy could allow us to cultivate the future demand for bioenergy for BECCS on the same, or less, land area than is currently used globally.

Abstract With the rapid advancement in wearable electronics, energy harvesting devices based on triboelectric nanogenerators (TENGs) have been intensively investigated for providing sustainable power supply for them. However, the fabrication of wearable TENGs still remains great challenges, such as flexibility, breathability and washability. Here, a route to develop a new kind of woven-structured triboelectric nanogenerator (WS-TENG) with a facile, low-cost, and scalable electrospinning technique is reported. The WS-TENG is fabricated with commercial stainless-steel yarns wrapped by electrospun polyamide 66 nanofiber and poly(vinylidenefluoride-co-trifluoroethylene) nanofiber, respectively. Triggered by diversified friction materials under a working principle of freestanding mode, the open-circuit voltage, short-circuit current and maximum instantaneous power density from the WS-TENG can reach up to 166 V, 8.5 µA and 93 mW/m2, respectively. By virtue of high flexibility, desirable breathability, washability and excellent durability, the fabricated WS-TENG is demonstrated to be a reliable power textile to light up 58 light-emitting diodes (LED) connected serially, charge commercial capacitors and drive portable electronics. A smart glove with stitched WS-TENGs is made to detect finger motion in different circumstances. The work presents a new approach for self-powered textiles with potential applications in biomechanical energy harvesting, wearable electronics and human motion monitoring.