Despite recent setbacks, solar remains a bright spot in the often wobbly renewable energy sector. In the U.S., the solar market is projected to top $22.90 billion by 2025, driven by falling materials costs and growing interest in offsite and rooftop installations. Moreover, in China -- the world's leading installer of solar panels and the largest producer of photovoltaic power -- 1.84 percent of the total electricity generated in the country two years ago came from solar. There's clearly growth -- which San Francisco startup Aurora Solar seeks to capitalize on with a novel solution combining lidar data, computer-assisted design, and computer vision. The company, which develops a suite of software that streamlines the solar panel installation process, today announced it has secured $20 million in a Series A round of financing from Energize Ventures, with contributions from S28 Capital and existing investor Pear.

Sense is a bright-orange box that sits in your electrical breaker box and gives in-depth insight into your home's entire power usage. The whole system is quite clever and--thankfully--free of any monthly charges. But it learns very slowly, and that's likely to frustrate you. Sense works by electromagnetically listening to the power flowing along the two hot wires that run from your electric meter to your breakers. By measuring the current flow a million times each second, Sense can observe changes in load with precise detail and, based on a machine-learning database, attempt to identify the footprint of different devices from the noise they generate. This means it can tell you exactly how much energy different appliances in your house use, and it does all of this without requiring sensors or smart plugs on each device.

Last month EAGE and PESGB organized the first machine learning workshop in geoscience in Europe. Clearly, I had every intention of going. And obviously, I met many of my favourite co-conspirators there, when I did. The workshop was divided between a day of keynotes and a day of technical talks. The keynotes accompanied the PETEX conference's last day.

With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments and streaming data. This paper aims to give a comprehensive survey of literature on hyperbox-based machine learning models. In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min-max neural networks, hyperbox-based hybrid models, and other algorithms based on hyperbox representation. Within each of these groups, this paper shows a brief description of the structure of models, associated learning algorithms, and an analysis of their advantages and drawbacks. Main applications of these hyperbox-based models to the real-world problems are also described in this paper. Finally, we discuss some open problems and identify potential future research directions in this field.

Energy disaggregation in a non-intrusive way estimates appliance level electricity consumption from a single meter that measures the whole house electricity demand. Recently, with the ongoing increment of energy data, there are many data-driven deep learning architectures being applied to solve the non-intrusive energy disaggregation problem. However, most proposed methods try to estimate the on-off state or the power consumption of appliance, which need not only large amount of parameters, but also hyper-parameter optimization prior to training and even preprocessing of energy data for a specified appliance. In this paper, instead of estimating on-off state or power consumption, we adapt a neural network to estimate the operational state change of appliance. Our proposed solution is more feasible across various appliances and lower complexity comparing to previous methods. The simulated experiments in the low sample rate dataset REDD show the competitive performance of the designed method, with respect to other two benchmark methods, Hidden Markov Model-based and Graph Signal processing-based approaches.

Security-Constrained Unit Commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via Mixed-Integer Linear Programming, sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning (ML) techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions and affine subspaces where the optimal solution is likely to lie, leading to significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that, using the proposed techniques, SCUC can be solved on average 12 times faster than conventional methods, with no negative impact on solution quality.

Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and high number of aging wind turbines. In particular, predictive maintenance planning requires early detection of faults with few false positives. This is a challenging problem due to the complex and weak signatures of some faults, in particular of faults occurring in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded over 46 months under typical industrial operations, thereby contributing novel test results and real world data that is made publicly available. Results of former studies addressing condition--monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on small sets of labeled data from test rigs operating under controlled conditions that focus on classification tasks, which are useful for quantitative method comparisons but gives little information about how useful these approaches are in practice. In this study dictionaries are learned from gearbox vibrations in six different turbines and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We perform the experiment using two different sparse coding algorithms to investigate if the algorithm selected affects the features of abnormal conditions. We calculate the dictionary distance between the initial and propagated dictionaries and find time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement. We also investigate the distance between dictionaries learned from geographically nearby

Photovoltaic (PV) power generation has emerged as one of the lead renewable energy sources. Yet, its production is characterized by high uncertainty, being dependent on weather conditions like solar irradiance and temperature. Predicting PV production, even in the 24 hour forecast, remains a challenge and leads energy providers to keep idle - often carbon emitting - plants. In this paper we introduce a Long-Term Recurrent Convolutional Network using Numerical Weather Predictions (NWP) to predict, in turn, PV production in the 24 hour and 48 hour forecast horizons. This network architecture fully leverages both temporal and spatial weather data, sampled over the whole geographical area of interest. We train our model on a NWP dataset from the National Oceanic and Atmospheric Administration (NOAA) to predict spatially aggregated PV production in Germany. We compare its performance to the persistence model and to state-of-the-art methods.

Artificial Intelligence has an important place in the scientific community as a result of its successful outputs in terms of different fields. In time, the field of Artificial Intelligence has been divided into many sub-fields because of increasing number of different solution approaches, methods, and techniques. Machine Learning has the most remarkable role with its functions to learn from samples from the environment. On the other hand, intelligent optimization done by inspiring from nature and swarms had its own unique scientific literature, with effective solutions provided for optimization problems from different fields. Because intelligent optimization can be applied in different fields effectively, this study aims to provide a general discussion on multidisciplinary effects of Artificial Intelligence by considering its optimization oriented solutions. The study briefly focuses on background of the intelligent optimization briefly and then gives application examples of intelligent optimization from a multidisciplinary perspective.

At CES earlier this month, GBatteries demonstrated that it could charge a 60-kwh battery pack, made up of off-the-shelf lithium-ion automotive cells, to half capacity in just 5 minutes, or to a full charge in 10 minutes. Further, the company is aiming, with a technology that employs AI elements, to boost charging speeds without accelerating degradation and rendering electric-vehicle battery packs useless. Such a technology could help lessen the effects of fast-charging battery packs in vehicles. The more often you fast-charge an electric-car battery pack--and the higher the charge rate--the higher the chances are that you'll do irreversible damage to the cells within, and decrease the cycle life of the battery and its effective capacity. DON'T MISS: Toyota and Panasonic to jointly make electric-car batteries, explore solid-state tech (Updated) The company's hardware and software solution together smartly speeds up or slows down charging momentarily, depending on conditions inside the battery.