Power Industry


Detecting Fake News in Social Media

Communications of the ACM

In March 2011, the catastrophic accident known as "The Fukushima Daiichi nuclear disaster" took place, initiated by the Tohoku earthquake and tsunami in Japan. The only nuclear accident to receive a Level-7 classification on the International Nuclear Event Scale since the Chernobyl nuclear power plant disaster in 1986, the Fukushima event triggered global concerns and rumors regarding radiation leaks. Among the false rumors was an image, which had been described as a map of radioactive discharge emanating into the Pacific Ocean, as illustrated in the accompanying figure. In fact, this figure, depicting the wave height of the tsunami that followed, still to this date circulates on social media with the inaccurate description. Social media is ideal for spreading rumors, because it lacks censorship.


Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

Neural Information Processing Systems

Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this paper, we propose to tackle such forecasting problem with Transformer. Although impressed by its performance in our preliminary study, we found its two major weaknesses: (1) locality-agnostics: the point-wise dot- product self-attention in canonical Transformer architecture is insensitive to local context, which can make the model prone to anomalies in time series; (2) memory bottleneck: space complexity of canonical Transformer grows quadratically with sequence length L, making directly modeling long time series infeasible. In order to solve these two issues, we first propose convolutional self-attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism. Then, we propose LogSparse Transformer with only O(L(log L) 2) memory cost, improving forecasting accuracy for time series with fine granularity and strong long-term dependencies under constrained memory budget.


Phoenix Air Unmanned seek VTOL UAS - sUAS News - The Business of Drones

#artificialintelligence

Phoenix Air Unmanned, LLC (PAU) is seeking information on the availability of Unmanned Aircraft Systems to support linear infrastructure inspections. The UAS will be operated by PAU who has been contracted by Xcel Energy as their unmanned flight service provider and they plan to purchase a minimum of 4 aircraft initially with the possibility of additional aircraft in the future. Xcel Energy, Inc. owns over 120,000 miles of transmission and distribution infrastructure across eight states (CO, MI, MN, NM, WI, ND, SD, TX) that must be inspected at regular intervals as required by state and federal regulations. Xcel Energy, Inc. is a utility holding company with a service company (Xcel Energy Services) and four wholly owned utility subsidiaries that serve electric and natural gas customers. PAU was established in 2014 for commercial UAS operations.


Phoenix Air Unmanned seek VTOL UAS - sUAS News - The Business of Drones

#artificialintelligence

Phoenix Air Unmanned, LLC (PAU) is seeking information on the availability of Unmanned Aircraft Systems to support linear infrastructure inspections. The UAS will be operated by PAU who has been contracted by Xcel Energy as their unmanned flight service provider and they plan to purchase a minimum of 4 aircraft initially with the possibility of additional aircraft in the future. Xcel Energy, Inc. owns over 120,000 miles of transmission and distribution infrastructure across eight states (CO, MI, MN, NM, WI, ND, SD, TX) that must be inspected at regular intervals as required by state and federal regulations. Xcel Energy, Inc. is a utility holding company with a service company (Xcel Energy Services) and four wholly owned utility subsidiaries that serve electric and natural gas customers. PAU was established in 2014 for commercial UAS operations.


Automated detection of pitting and stress corrosion cracks in used nuclear fuel dry storage canisters using residual neural networks

arXiv.org Machine Learning

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of pitting and stress corrosion cracking, with a focus on dry storage canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion cracks via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.


How to Make industrial AI Work in Extreme Conditions?

#artificialintelligence

Artificial Intelligence (AI) can be applied to a lot of industrial environments to save costs and to improve processes. This industrial Artificial Intelligence does not only include the smart algorithms and Big Data concepts that reside in the virtual space inside the computer systems, but it consists of the physical devices themselves too. Data has to be captured with sensors. Commands have to be sent to actuators and control systems. This whole chain and flow of information, wireless or via cables, goes through places with extreme conditions.


Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study

arXiv.org Machine Learning

Pattern similarity-based methods are widely used in classification and regression problems. Repeated, similar-shaped cycles observed in seasonal time series encourage to apply these methods for forecasting. In this paper we use the pattern similarity-based methods for forecasting monthly electricity demand expressing annual seasonality. An integral part of the models is the time series representation using patterns of time series sequences. Pattern representation ensures the input and output data unification through trend filtering and variance equalization. Consequently, pattern representation simplifies the forecasting problem and allows us to use models based on pattern similarity. We consider four such models: nearest neighbor model, fuzzy neighborhood model, kernel regression model and general regression neural network. A regression function is constructed by aggregation output patterns with weights dependent on the similarity between input patterns. The advantages of the proposed models are: clear principle of operation, small number of parameters to adjust, fast optimization procedure, good generalization ability, working on the newest data without retraining, robustness to missing input variables, and generating a vector as an output. In the experimental part of the work the proposed models were used to forecasting the monthly demand for 35 European countries. The model performances were compared with the performances of the classical models such as ARIMA and exponential smoothing as well as state-of-the-art models such as multilayer perceptron, neuro-fuzzy system and long short-term memory model. The results show high performance of the proposed models which outperform the comparative models in accuracy, simplicity and ease of optimization.


Opinion: How Blockchain Will Power The Electricity Grid of The Future

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Earlier this month, under its "The Future of Everything" vertical, The Wall Street Journal reported how AI is improving the power grid. The Journal says artificial intelligence is "the key to keeping the lights on." The article explains how power companies are "turning to AI, drones, and sensors to curtail outages, save money, and help operate an increasingly complex electricity grid." Further, by doing so, they cut the recovery time from hurricane-related outages nearly in half in just a little over a decade. But these improvements are just the beginning of how artificial intelligence will manage the electricity grid of the future.


Online Hierarchical Forecasting for Power Consumption Data

arXiv.org Machine Learning

We study the forecasting of the power consumptions of a population of households and of subpopulations thereof. These subpopulations are built according to location, to exogenous information and/or to profiles we determined from historical households consumption time series. Thus, we aim to forecast the electricity consumption time series at several levels of households aggregation. These time series are linked through some summation constraints which induce a hierarchy. Our approach consists in three steps: feature generation, aggregation and projection. Firstly (feature generation step), we build, for each considering group for households, a benchmark forecast (called features), using random forests or generalized additive models. Secondly (aggregation step), aggregation algorithms, run in parallel, aggregate these forecasts and provide new predictions. Finally (projection step), we use the summation constraints induced by the time series underlying hierarchy to re-conciliate the forecasts by projecting them in a well-chosen linear subspace. We provide some theoretical guaranties on the average prediction error of this methodology, through the minimization of a quantity called regret. We also test our approach on households power consumption data collected in Great Britain by multiple energy providers in the Energy Demand Research Project context. We build and compare various population segmentations for the evaluation of our approach performance.


Artificial intelligence Part 3: Real Grid-Operations Benefits Aclara Blog

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In Part 3 of our series on how utilities are using artificial intelligence, we look at how AI amplifies analytics for grid operations. Duke Energy saved some $130 million in avoided costs by using predictive data analytics to identify problems before they caused equipment failures. A utility in Brazil estimates savings in the range of $420,000 USD each month through better, analytics-based theft detection. Because, as an article published by Forbes notes, "Machine learning is a continuation of the concepts around predictive analytics, with one key difference: The AI system is able to make assumptions, test and learn autonomously." With these enhancements, data science will become more powerful than ever, and utilities stand to gain.