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

Cox, Sarah; Lopez, Anthony; Watson, Andrea; Grue, Nicholas W.; Leisch, Jennifer

doi:10.2172/1427970

Cited by 15 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LG (x, y) = Lcontent(x, y) + α L adversarial (x, y), [6] where α ∈ R. The adversarial loss is from Eq. 5 and captures the ability of the generator network to fool the discriminator in accordance with the minmax problem defined in Eq.…”

Section: Methodsmentioning

confidence: 99%

“…The typical GCM configuration used in production runs has a resolution of around 1 • , or about 100 km close to the equator. Such a resolution is insufficient to accurately assess renewable energy resources, which typically require resolution finer than 10 km, preferably 2 km (6). Consequently, there is a great need for efficient and physically accurate methods to enhance the resolution of GCM output for studying the energy impact of different climate scenarios.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Adversarial super-resolution of climatological wind and solar data

Stengel

Glaws

Hettinger

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

159

123

View full text Add to dashboard Cite

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50× resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

show abstract

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Adversarial super-resolution of climatological wind and solar data

Stengel

Glaws

Hettinger

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

159

123

View full text Add to dashboard Cite

show abstract

“…Table 2 reviews different input vectors of renewable energy based on training model and validation for solar energy, wind power, and hydro-power prediction in specific [108]. Prediction of solar energy needs a maximum of nine parameters, such as sun altitude, latitude/longitude, time (including month and year), an average of ambient temperature, mean air pressure, the average speed of the wind, and means air's humidity.…”

Section: Data Description and Processingmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…For more information on temporal data and other data to support renewable energy analysis and decisions, see Cox et al (2018).…”

Section: Time Series Data Download Toolmentioning

confidence: 99%

“…• Tutorials that walk users through the RE Data Explorer and analyses in a guided format • Technical potential webinars and other training resources • A decision guide (Cox et al 2018) to support links across data, analysis, and decision-making for renewable energy development • Information on data included in the RE Data Explorer for each country as well as a spreadsheet to help users assess the data needs for analyses within the RE Data Explorer.…”

Section: Support Resourcesmentioning

confidence: 99%