The challenges of achieving a 100% renewable electricity system in the United States

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

Global atlas of solar and wind resources temporal complementarity

Modeling variable renewable energy and storage in the power sector

OpenSolar: Promoting the openness and accessibility of diverse public solar datasets

/pdf/renewable-energy-data-analysis-and-decisions-a-guide-for-z49ngroqcw.pdf

Renewable Energy Data, Analysis, and Decisions: A Guide for Practitioners

/pdf/will-solar-panels-help-when-the-power-goes-out-planning-for-1tc0aq3yap.pdf

Will Solar Panels Help When the Power Goes Out? Planning for PV Resilience

In 2018, nearly two trillion U.S. dollars were invested in the global energy sector, with clean energy investment totaling over $332 billion (BNEF 2019) (IEA 2019). Despite a 1% decrease from 2017, global investment in the power sector was once again larger than in the oil and gas sector for the third year in a row (IEA 2019). Forecasted to meet more than 70% of global electricity generation growth over the next five years, renewables are projected to have the fastest growth in the electricity sector, providing almost 30% of power demand in 2023 (IEA 2018a). In 2018, total global renewable energy capacity reached 2,351 gigawatts (GW), an increase of 7.9% from 2017 (IRENA 2019). Solar and wind technologies continued to drive the increase in renewable generation capacity, accounting for 84% of new capacity (IRENA 2019). Solar was the top source of new power capacity in several major markets, with nonrenewable generation capacity decreasing in Europe, North America, and Oceania since 2010, with consistent reductions in capacity each year in Europe, and reductions in four of the last eight years in the other two regions (IRENA 2019). With sustained solar growth in the region, China, India, Japan, and the Republic of Korea were the main contributors to the 64-GW increase in global solar capacity; China and the United States were the primary contributors to wind energy expansion.

/pdf/utility-scale-wind-and-solar-in-emerging-markets-private-1newf9dfkn.pdf

Utility-Scale Wind and Solar in Emerging Markets - Private Sector Perspectives

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Andrea C. Watson

Author Tools

Chat about Author

Papers

Renewable Energy Data, Analysis, and Decisions: A Guide for Practitioners

Will Solar Panels Help When the Power Goes Out? Planning for PV Resilience

Utility-Scale Wind and Solar in Emerging Markets - Private Sector Perspectives