ABSTRACT
The timing of electricity consumption is increasingly important for grid operations. In response, households are being encouraged to alter their daily usage patterns through demand response and time-varying pricing, although it is unknown if they are aware of these patterns. Here we introduce an energy literacy concept, 'load shape awareness', and apply it to a sample of California residents (n = 186) who provided their household's hourly electricity data and completed an energy use questionnaire. Choosing from four prominent load shape designations, half of respondents (51%) correctly identified their dominant load shape before COVID-19 shelter-in-place (SIP) orders while only one-third (31%) did so during SIP orders. Those aware of their load shape were more likely to have chosen evening peak, the most frequent dominant shape in the electricity data. Our work provides proof of principle for the load shape awareness concept, which could prove useful in designing energy conservation interventions and helping consumers adapt to an evolving energy system. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.
ABSTRACT
The electric grid is uniquely susceptible to extreme events through both power supply and consumption pathways. Extreme events - like heatwaves and droughts - are expected to increase in frequency and severity due to climate change and are already causing consequences on power system operations and stability. Additionally, non-climate related events like the COVID-19 pandemic have had dramatic impacts on energy consumption patterns globally. We apply modern machine learning methods to model electricity consumption in Brazil, one of the largest generators of hydropower, to better understand the consumption-side effects of extreme national and regional events. After training on 20 years of historical data, we verify an R2of 0.848 and a MAPE of 2.6% for our counterfactual model and use it to assess impacts of historical events on electricity consumption. We then discuss how this approach can be applied toward measuring energy system responsiveness and resiliency on present and future scenarios. © 2022 IEEE.
ABSTRACT
When confronted with crises and other extreme events, the core responsibility of the grid does not change: to meet society's demand for electricity in a safe and reliable manner. However, these extreme events are now interacting with a transitioning grid system that is constantly evolving and adapting to meet new societal needs. This change includes the decarbonization of energy systems while also ensuring equity in its reliability, accessibility, and affordability of energy for all. In this article, we examine a single extreme event with unprecedented impacts on the energy system: the COVID-19 pandemic. Using California as an example, we explore in detail the pandemic-related impacts on electricity consumption and make recommendations for improving planning, forecasting, and other operations in response to extremes. © 2003-2012 IEEE.
ABSTRACT
Lung disease is a most common disease all over the world. A numerous feature extraction with classification models were discussed previously about the lung disease, but those methods having high over fitting problem, consequently, decrease the accuracy of detection. To overwhelm this issue, a Deep Convolutional Spiking Neural Network optimized with Arithmetic Optimization Algorithm is proposed in this manuscript for Lung Disease Detection using Chest X-ray Images as COVID-19, normal and viral pneumonia. Initially, NIH chest X-ray image dataset is taken from Kaggle repository for detecting lung disease. Then, the chest X-ray images are pre-processed using the Anisotropic Diffusion Filter Based Unsharp Masking and crispening scheme for removing noise and enhancing the image quality. These pre-processed outputs are fed to feature extraction. In feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Deep Convolutional Spiking Neural Network classifier (DCSNN) for detecting lung diseases. Here, the weight with bias parameter of DCSNN is enhanced based upon Arithmetic Optimization Algorithm (AOA), which improves detection accuracy. The simulation is executed in MATLAB. The proposed LDC-DCSNN-AOA technique attains higher accuracy, higher Precision, higher F-Score analyzed with the existing techniques, like Lung disease detection using Support Vector Machines optimized with Social Mimic Optimization (LDC-SVM-SMO), Lung disease detection using eXtreme Gradient Boosting optimized by particle swarm optimization (LDC-XGBoost-PSO), Lung disease detection using neuro-fuzzy classifier optimized with multi-objective genetic algorithm (LDC-NFC-MOGA), Lung disease detection using convolutional neural network optimized with Bayesian optimization LDC –CNN-BOA respectively.
ABSTRACT
To contain the spread of the novel coronavirus (COVID-19), local and state governments in the U.S. have imposed restrictions on daily life, resulting in dramatic changes to how and where people interact, travel, socialize, and work. Using a social practice perspective, we explore how California's Shelter-in-Place (SIP) order impacted household energy activities. To do so, we conducted an online survey of California residents (n = 804) during active SIP restrictions (May 5-18, 2020). We asked respondents about changes to home occupancy patterns and household energy activities (e.g., cooking, electronics usage) due to SIP restrictions, as well as perspectives toward smart energy technologies. Households reported increased midday (10am-3pm) occupancy during SIP, and this increase is related to respondent and household characteristics, such as education and the presence of minors in the home. Examining change in the frequency of household activities during SIP, presence of minors and increased midday occupancy proved important. Finally, we considered relationships to intention to purchase smart home technologies, with the presence of minors and increased activity frequency relating to greater intention to purchase. These findings demonstrate how household activities and occupancy changed under COVID restrictions, how these changes may be related to energy use in the home, and how such COVID-related changes could be shaping perspectives toward smart home technology, potentially providing insight into future impacts on household practices and electricity demand.