Climate, weather, socio-economic and electricity usage data for the residential and commercial sectors in FL, U.S.
ABSTRACT: This paper presents the data that is used in the article entitled "Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States" (Mukhopadhyay and Nateghi, 2017) . The data described in this paper pertains to the state of Florida (during the period of January 1990 to November 2015). It can be classified into four categories of (i) state-level electricity consumption data; (ii) climate data; (iii) weather data; and (iv) socio-economic data. While, electricity consumption data and climate data are obtained at monthly scale directly from the source, the weather data was initially obtained at daily-level, and then aggregated to monthly level for the purpose of analysis. The time scale of socio-economic data varies from monthly-level to yearly-level. This dataset can be used to analyze the influence of climate and weather on the electricity demand as described in Mukhopadhyay and Nateghi (2017) .
Project description:Estimating the impact of climate change on energy use across the globe is essential for analysis of both mitigation and adaptation policies. Yet existing empirical estimates are concentrated in Western countries, especially the United States. We use daily data on household electricity consumption to estimate how electricity consumption would change in Shanghai in the context of climate change. For colder days <7 °C, a 1 °C increase in daily temperature reduces electricity consumption by 2.8%. On warm days >25 °C, a 1 °C increase in daily temperatures leads to a 14.5% increase in electricity consumption. As income increases, households' weather sensitivity remains the same for hotter days in the summer but increases during the winter. We use this estimated behavior in conjunction with a collection of downscaled global climate models (GCMs) to construct a relationship between future annual global mean surface temperature (GMST) changes and annual residential electricity consumption. We find that annual electricity consumption increases by 9.2% per +1 °C in annual GMST. In comparison, annual peak electricity use increases by as much as 36.1% per +1 °C in annual GMST. Although most accurate for Shanghai, our findings could be most credibly extended to the urban areas in the Yangtze River Delta, covering roughly one-fifth of China's urban population and one-fourth of the gross domestic product.
Project description:The data presented in this article are related to the research article entitled: "Information strategies for energy conservation: a field experiment in India" (Chen et al., 2017) . The availability of high-resolution electricity data offers benefits to both utilities and consumers to understand the dynamics of energy consumption for example, between billing periods or times of peak demand. However, few public datasets with high-temporal resolution have been available to researchers on electricity use, especially at the appliance-level. This article describes data collected in a residential field experiment for 19 apartments at an Indian faculty housing complex during the period from August 1, 2013 to May 12, 2014. The dataset includes detailed information about electricity consumption. It also includes information on apartment characteristics and hourly weather variation to enable further studies of energy performance. These data can be used by researchers as training datasets to evaluate electricity usage consumption.
Project description:There is growing empirical evidence that anthropogenic climate change will substantially affect the electric sector. Impacts will stem both from the supply side-through the mitigation of greenhouse gases-and from the demand side-through adaptive responses to a changing environment. Here we provide evidence of a polarization of both peak load and overall electricity consumption under future warming for the world's third-largest electricity market-the 35 countries of Europe. We statistically estimate country-level dose-response functions between daily peak/total electricity load and ambient temperature for the period 2006-2012. After removing the impact of nontemperature confounders and normalizing the residual load data for each country, we estimate a common dose-response function, which we use to compute national electricity loads for temperatures that lie outside each country's currently observed temperature range. To this end, we impose end-of-century climate on today's European economies following three different greenhouse-gas concentration trajectories, ranging from ambitious climate-change mitigation-in line with the Paris agreement-to unabated climate change. We find significant increases in average daily peak load and overall electricity consumption in southern and western Europe (?3 to ?7% for Portugal and Spain) and significant decreases in northern Europe (?-6 to ?-2% for Sweden and Norway). While the projected effect on European total consumption is nearly zero, the significant polarization and seasonal shifts in peak demand and consumption have important ramifications for the location of costly peak-generating capacity, transmission infrastructure, and the design of energy-efficiency policy and storage capacity.
Project description:The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the US becoming the epicenter of COVID-19 cases since late March. As the US begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing US wholesale electricity markets with COVID-19 case, weather, mobile device location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we rigorously uncover a significant reduction in electricity consumption that is strongly correlated with the number of COVID-19 cases, degree of social distancing, and level of commercial activity.
Project description:The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. The unique multi-source composition of the dataset makes it an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems including energy consumption, mobility planning, tourist and migrant flows, urban structures and interactions, event detection, urban well-being and many others.
Project description:Understanding electricity consumption and production patterns is a necessary first step toward reducing the health and climate impacts of associated emissions. In this work, the economic input-output model is adapted to track emissions flows through electric grids and quantify the pollution embodied in electricity production, exchanges, and, ultimately, consumption for the 66 continental US Balancing Authorities (BAs). The hourly and BA-level dataset we generate and release leverages multiple publicly available datasets for the year 2016. Our analysis demonstrates the importance of considering location and temporal effects as well as electricity exchanges in estimating emissions footprints. While increasing electricity exchanges makes the integration of renewable electricity easier, importing electricity may also run counter to climate-change goals, and citizens in regions exporting electricity from high-emission-generating sources bear a disproportionate air-pollution burden. For example, 40% of the carbon emissions related to electricity consumption in California's main BA were produced in a different region. From 30 to 50% of the sulfur dioxide and nitrogen oxides released in some of the coal-heavy Rocky Mountain regions were related to electricity produced that was then exported. Whether for policymakers designing energy efficiency and renewable programs, regulators enforcing emissions standards, or large electricity consumers greening their supply, greater resolution is needed for electric-sector emissions indices to evaluate progress against current and future goals.
Project description:It has been suggested that climate change impacts on the electric sector will account for the majority of global economic damages by the end of the current century and beyond [Rose S, et al. (2014) Understanding the Social Cost of Carbon: A Technical Assessment]. The empirical literature has shown significant increases in climate-driven impacts on overall consumption, yet has not focused on the cost implications of the increased intensity and frequency of extreme events driving peak demand, which is the highest load observed in a period. We use comprehensive, high-frequency data at the level of load balancing authorities to parameterize the relationship between average or peak electricity demand and temperature for a major economy. Using statistical models, we analyze multiyear data from 166 load balancing authorities in the United States. We couple the estimated temperature response functions for total daily consumption and daily peak load with 18 downscaled global climate models (GCMs) to simulate climate change-driven impacts on both outcomes. We show moderate and heterogeneous changes in consumption, with an average increase of 2.8% by end of century. The results of our peak load simulations, however, suggest significant increases in the intensity and frequency of peak events throughout the United States, assuming today's technology and electricity market fundamentals. As the electricity grid is built to endure maximum load, our findings have significant implications for the construction of costly peak generating capacity, suggesting additional peak capacity costs of up to 180 billion dollars by the end of the century under business-as-usual.
Project description:The time-series dataset presented in this article was captured using a real-time energy monitoring device from a distribution panel of a student residence in Johannesburg, South Africa. The data was captured from April 2016 to January 2018. The data from the three conductors supplying the student residence with electricity was automatically aggregated and presented as a single data point. The granularity was at resolution levels of watt-minute and kilowatt-hour. A total of 13,966 hrs of data points was captured. The data has not been processed further. Hence, data consists of 1,209-hour of missing data points. In addition to the energy consumption data, 16 months of hourly data for wind speed, temperature and humidity of the closest weather station has been provided. The data will be useful in the formulation of mathematical models of electricity consumption that is most suitable for a student residence. Furthermore, the data provided in this article will encourage the development of a data-driven electricity consumption management strategy and policy formulation for student residences.
Project description:Microgrids comprising renewable energy technologies are often modelled and optimised from a theoretical point of view. Verification of theoretical systems with data of actually implemented systems in the field rarely occurs in an open manner, especially on the intermediate scale of research buildings. To enable modelling of the actual microgrid performance of a research environment, we present a multiyear dataset of a microgrid with solar arrays and a battery. The main energy datasets comprise data per second supplemented by hourly solar irradiation data. These may be combined with data concerning the hourly electricity prices from the main grid and the low-electricity-price periods of national holidays. The level of detail of the data per second in combination with the hourly data in these datasets allows for a comparison to the efficiency and weather-parameter correlation of other renewable energy technologies, as well as forecasting future energy generation and consumption.
Project description:In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers.