In this paper we present a novel agent-based modeling methodology to predict rooftop solar adoptions in the residential energy market. We first applied several linear regression models to estimate missing variables for non-adopters, so that attributes of non-adopters and adopters could be used to train a logistic regression model. Then, we integrated the logistic regression model along with other predictive models into a multi-agent simulation platform and validated our models by comparing the forecast of aggregate adoptions in a typical zip code area with its ground truth. This result shows that the agent-based model can reliably predict future adoptions. Finally, based on the validated agent-based model, we compared the outcome of a hypothesized seeding policy with the original incentive plan, and investigated other alternative seeding policies which could lead to more adopters.

Behavioral scientists contend that individuals, and organizations rarely make decisions solely on the basis of economic factors. Decisions are also shaped by perceived risk, social interactions, currency and salience of information, and other value propositions. Social diffusion of information on consumer experiences, entrance of new business models better aligned with customers' concerns when evaluating investments, and perceived improving economic conditions are all factors in consumers' decisions to adopt a new technology, such as solar photovoltaics (PV). We describe a new conceptual agent-based model, BE-Solar, that incorporates a social and behavioral decision framework for technology adoption decisions. We demonstrate the feasibility of including heterogeneity and behavioral factors into an agent-based model of the solar PV market, which is being applied to the Southern California market.

Invertible neural networks (INNs) such as coupling flows are firstly introduced as a class of generative models with a tractable likelihood [11, 25, 40], and have shown their usefulness and powerfulness in various machine learning tasks such as inverse problems [2], probabilistic inference [29] and feature extraction [22] in recent years. With plenty of successful applications of INNs, one would wonder if such a type of models have the universal expressiveness. As most generative models mainly concern about the transform between distributions, existing works such as [19, 23] focused on the expressiveness from the distribution perspective. However, the expressiveness from the distribution perspective does not result in the expressiveness from the mapping perspective, as there are a large (or even infinite) number of diffeomorphisms mapping the given source µ to the given target ν. In many applications, knowing the distributional universality is not yet enough, one may be interested in knowing if the optimal transport [41], which finds emerging applications in many fields, e.g., machine learning [32], wireless communication [30] and economics [15], can be approximated by invertible neural networks.

Demand response (DR), as one of the important energy resources in the future's grid, provides the services of peak shaving, enhancing the efficiency of renewable energy utilization with a short response period, and low cost. Various categories of DR are established, e.g. automated DR, incentive DR, emergency DR, and demand bidding. However, with the practical issue of the unawareness of residential and commercial consumers' utility models, the researches about demand bidding aggregator involved in the electricity market are just at the beginning stage. For this issue, the bidding price and bidding quantity are two required decision variables while considering the uncertainties due to the market and participants. In this paper, we determine the bidding and purchasing strategy simultaneously employing the smart meter data and functions. A two-agent deep deterministic policy gradient method is developed to optimize the decisions through learning historical bidding experiences. The online learning further utilizes the daily newest bidding experience attained to ensure trend tracing and self-adaptation. Two environment simulators are adopted for testifying the robustness of the model. The results prove that when facing diverse situations the proposed model can earn the optimal profit via off/online learning the bidding rules and robustly making the proper bid.

Our cities can no longer afford to be at war with nature: they need to rapidly become places where people and nature co-exist and thrive. Fortunately, there is growing recognition that nature-based solutions to cities' various challenges offer far wider benefits than traditional engineered'grey' solutions: including improving resilience, better health for its citizens, and a faster path to net zero. In our recent report with the World Economic Forum, BiodiverCities by 2030: Transforming Cities' Relationship with Nature, we highlighted that nature-based solutions are on average 50% more cost-effective than purely man-made alternatives, and deliver 28% more added value in both direct and environmental benefits. But what will wean us off our addiction to'grey' traditional concrete solutions, and move us towards approaches that better regenerate nature and reduce carbon? I believe that the innovation and fresh opportunities that come from using advanced digital tools can provide the answer.

Mike Ananny walked his dog this morning. He did so with no expectation of privacy. "I know that I was subject to a wide variety of cameras, whether it's Ring doorbells, cars driving along, or even city traffic cameras," he said. "I didn't choose to participate in this whole variety of video surveillance systems. I just took my dog for a walk." Ananny understands that, wherever he goes, data about him is being collected, analyzed and monetized by artificial intelligence (AI). Kate Crawford drove a van deep into the arid Nevada landscape to get a good look at the evaporating brine ponds of the Silver Peak Lithium Mine.

Image Processing vs Computer Vision 7. Image Processing Example 1) Rescaling: zoom in, zoom out, cropping 2) Correcting illumination: brightness and contrast 3) Color manipulations: black and white, gray-scale, HSV, RGB, BGR 4) Filter: mean, median, low pass, high pass, gaussian, laplacian 5) Edge detection Original Sobel Laplacian Canny 8. Image Processing Example 6) Morphology: - Erode (local minimum) - Dilate (local maximum) - Opening (erosion then dilation) - Closing (dilation then erosion) - Morphology Gradient (MG) (difference between dilation and erosion) - Top Hat (TH) (difference between image and opening) - Black Hat (difference between image and closing) 9. Computer Vision Example Scene modeling Object detection Object recognition Object tracking Pose estimation Motion estimation Image restoration 10. Artificial Intelligence (AI) AI is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals.

Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo. However, SVGD has been found to suffer from variance underestimation when the dimensionality of the target distribution is high. Recent developments have advocated projecting both the score function and the data onto real lines to sidestep this issue, although this can severely overestimate the epistemic (model) uncertainty. In this work, we propose Grassmann Stein variational gradient descent (GSVGD) as an alternative approach, which permits projections onto arbitrary dimensional subspaces. Compared with other variants of SVGD that rely on dimensionality reduction, GSVGD updates the projectors simultaneously for the score function and the data, and the optimal projectors are determined through a coupled Grassmann-valued diffusion process which explores favourable subspaces. Both our theoretical and experimental results suggest that GSVGD enjoys efficient state-space exploration in high-dimensional problems that have an intrinsic low-dimensional structure.

WHEN the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned. As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper published in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time. "The brains of living beings could continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan," said Shriram Ramanathan, a professor in Purdue University's School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.

The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late 2019 as one of the largest machine learning competitions ever held focused on building performance. It was hosted on the Kaggle platform and resulted in 39,402 prediction submissions, with the top five teams splitting $25,000 in prize money. This paper outlines lessons learned from participants, mainly from teams who scored in the top 5% of the competition. Various insights were gained from their experience through an online survey, analysis of publicly shared submissions and notebooks, and the documentation of the winning teams. The top-performing solutions mostly used ensembles of Gradient Boosting Machine (GBM) tree-based models, with the LightGBM package being the most popular. The survey participants indicated that the preprocessing and feature extraction phases were the most important aspects of creating the best modeling approach. All the survey respondents used Python as their primary modeling tool, and it was common to use Jupyter-style Notebooks as development environments. These conclusions are essential to help steer the research and practical implementation of building energy meter prediction in the future.