Offshore wind development is of rapidly growing interest on the US Eastern seaboard where potentially strong wind energy resource is located in proximity to high priced energy demand centers. However, there is little historic, robust, publicly available wind resource data to guide developers and investors in making informed decisions about the specific siting of offshore wind farms, the wind farm layout, the turbine equipment ...more »
Using Existing Datasets
As of recently, energy consumption at the residential level has become a hot topic of discussion within the United States. Although we have collectively made great progress over the past decade in becoming more environmentally conscious, we still have room for tremendous improvement. The U.S. government has recognized the need to become more energy efficient by creating subsidies in specific industries such as renewables, ...more »
I believe that many would appreciate any datafeed mechanism that would allow to automatically import and update sets of EIA data to MS Excel or similar using common ways of doing that.
Households and businesses choose to be more efficient and “green” if extra effort and money leads to significant savings. The average professional may not be focused on reducing his or her carbon footprint but will be motivated by incentive. The Database of State Incentives for Renewable Energy (DSIRE) addresses this issue by allowing residents and businesses to access financial energy incentives in order to save money ...more »
My video describes a mathematical model which predicts which states would best host new solar energy rooftop photovoltaic system installation businesses. It is meant to promote efficient growth of the solar energy market.
Of the four Topic datasets listed in the NLE( National Energy Library That encompass all available energy data sets in the DOE complex) ...more »
Most who visit the EIA, do so to download data into a spreadsheet. For those researches, analysts, and businesses that don't know VBA but wish to retrieve EIA data, we have to go online and re-download it or manually input data each time it's updated- weekly/ monthly/ annually. The EIA's charting feature is helpful, many prefer to manipulate the data themselves, in their own spreadsheets, which is quite time consuming ...more »
As energy researchers, the Department of Energy datasets are the foundation of our work. Choosing the most valuable among them is like being asked to choose the most valuable book from a library – the value comes from the library itself, not the single book. Yet, no dataset in recent memory has proved more important than OpenEI.gov’s new Transparent Cost Database (http://en.openei.org/apps/TCDB/). This database displays ...more »
We propose a Home Energy Score API to allow home buyers and renters to know the energy performance of a home as part of the purchasing or rental decision. The home energy score should be available on home purchasing sites like MLS, Realtor.com, and Zillow and on apartment rental sites like Rent.com, Airbnb.com and Apartments.com. This information should be as easily accessible as the miles per gallon rating on vehicles ...more »
Develop predictive analytics software to forecast near-future energy pricing by extracting data from the United States Energy Information Administration (EIA) website including the historical prices of gasoline, crude oil, and electricity. Based upon these historical energy pricing trends, predicted weather patterns, location, and Monte-Carlo analysis that predicts future geopolitical events, extreme weather events, discovery ...more »
In America, Energy Awareness is still a challenge. Department of Energy has done an excellent job of consolidating energy information. In particular, DSIREUSA.ORG is an excellent tool that identifies the incentives in the respective regional. What we lack is awareness and quantifying the energy usage through the day, week, weekends, and seasons, year over year by various energy users and our facilities energy profile ...more »
Hi United States of America.
You cuzzies should compile your demand by us suburb and location type and capacity of your plants and their marginal cost. use this data to do some sweet network optimisation so your low and high marginal cost plants are located optimally to reduce your transmission losses.
Although all you guys are pretty smart and I imagine of already do this.