1,689 research outputs found
Biologically Inspired Dynamic Textures for Probing Motion Perception
Perception is often described as a predictive process based on an optimal
inference with respect to a generative model. We study here the principled
construction of a generative model specifically crafted to probe motion
perception. In that context, we first provide an axiomatic, biologically-driven
derivation of the model. This model synthesizes random dynamic textures which
are defined by stationary Gaussian distributions obtained by the random
aggregation of warped patterns. Importantly, we show that this model can
equivalently be described as a stochastic partial differential equation. Using
this characterization of motion in images, it allows us to recast motion-energy
models into a principled Bayesian inference framework. Finally, we apply these
textures in order to psychophysically probe speed perception in humans. In this
framework, while the likelihood is derived from the generative model, the prior
is estimated from the observed results and accounts for the perceptual bias in
a principled fashion.Comment: Twenty-ninth Annual Conference on Neural Information Processing
Systems (NIPS), Dec 2015, Montreal, Canad
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Data-Driven Control, Modeling, and Forecasting for Residential Solar Power
Distributed solar generation is rising rapidly due to a continuing decline in the cost of solar modules. Most residential solar deployments today are grid-tied, enabling them to draw power from the grid when their local demand exceeds solar generation and feed power into the grid when their local solar generation exceeds demand. The electric grid was not designed to support such decentralized and intermittent energy generation by millions of individual users. This dramatic increase in solar power is placing increasing stress on the grid, which must continue to balance its supply and demand despite the potential for large solar fluctuations. To address the problem, this thesis proposes new data-driven techniques for better controlling, modeling, and forecasting residential solar power.
The grid currently exercises no direct control over its connected solar capacity, but instead indirectly controls it by regulating new solar connections. This approach is highly inefficient and wastes much of the grid\u27s potential to transmit solar. Instead, we propose Software-defined Solar-powered (SDS) systems that dynamically regulate solar flow rates into the grid and design an SDS prototype, called SunShade. Specifically, we introduce a new class of Weighted Power Point Tracking (WPPT) algorithms, inspired by Maximum Power Point Tracking (MPPT), capable of dynamically enforcing both hard and relative caps on solar power, which enables the grid to decouple rate control from admission control. In contrast, to avoid grid regulations entirely, homes can also partially or entirely defect from the grid to fully utilize their solar power without restrictions. We present a switching architecture that enables homes to dynamically switch between a local generator, battery, and solar to co-optimize their cost, carbon footprint, switching frequency, and reliability. We introduce switching policies that reveal a tradeoff between solar utilization and reliability, such that higher solar utilization requires more switching, which can lead to lower reliability.
Enabling better control of intermittent solar also requires improving solar performance models, which infer solar output based on current environmental conditions. Recent solar models primarily leverage data from ground-based weather stations, which may be far from solar sites and thus inaccurate. In addition, these weather stations report cloud cover---the most important metric for solar modeling---in coarse units of oktas. Instead, we propose developing solar models based on data from a new generation of Geostationary Operational Environmental Satellites (GOES-16 and GOES-17) that began launching in late 2017. We develop physical and machine learning (ML) models for solar performance modeling using both derived data products released by the National Oceanic and Atmospheric Administration (NOAA), as well as the satellites\u27 raw multispectral data. We find that ML-based models using the raw multispectral data are significantly more accurate than both physical models using derived data products, such as Downward Shortwave Radiation (DSR), and prior okta-based solar models. The raw multispectral data is also beneficial since it is available at much higher spatial and temporal resolutions---1km^2 and every 5 minutes---than oktas---25km^2 and every hour. The accuracy of our ML-based models on multispectral data is also better regardless of whether they are locally trained using data only from a particular solar site or globally trained using data from many solar sites. Since global models can be trained once but used anywhere, they can also enable accurate modeling for sites with limited data, e.g., newly installed solar sites.
Solar forecasting models, which predict future solar output based on environmental conditions also help in better solar control. Accurate near-term solar forecasts on the order of minutes to an hour are particularly important because homes and the grid must be able to adapt to large sudden changes in solar output. Current solar forecasting techniques, which primarily use Numerical Weather Predictions (NWP) algorithms, mostly leverage physics-based modeling. These physics-based models are most appropriate for forecast horizons on the order of hours to days and not near-term forecasts on the order of minutes to an hour. While there is some recent work on analyzing images from ground-based sky cameras for accurate near-term solar forecasting, it requires installing additional infrastructure. We instead propose a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Specifically, we develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across multiple locations to predict raw future observations of the spatio-temporal data collected by the recently launched GOES-R series of satellites. Our model estimates a location\u27s future solar irradiance based on satellite observations, which we feed to a regression model trained on smaller site-specific solar data to provide near-term solar photovoltaic (PV) forecasts that account for site-specific characteristics
Centralized solar PV generation forecast from the perspective of a distribution system operator
Tese de mestrado integrado, Engenharia da Energia e do Ambiente, Universidade de Lisboa, Faculdade de Ciências, 2018It is essential to have mechanisms to promote the integration of electricity from renewable energy sources in the power system from a technical, economic and social perspectives. Due to the stochastic nature of photovoltaic generation, good forecasts of future generation help grid operators and individual producers to better manage their operations, thus increasing the PV efficiency and competitiveness. This dissertation describes the development of a Random Forests forecasting algorithm for electricity generation of a photovoltaic power-plant from the perspective of Distribution System Operator. The model developed has the final aim to be a tool as support for grid management. The forecasting techniques chosen were Persistence and Random Forests. The inputs include a 3x3 matrix of weather forecasts, performed by a Numeric Weather Prediction model (centered on the location of the power-plant) astronomical and time variables. Two models were created: a Day-ahead model and an Intraday model. The Day-ahead model performs an hourly forecast early in the day using data from the previous day, while the Intraday is updated during the day, including photovoltaic generation data to correct the forecast made earlier by the Day-ahead model. Both models produce forecasts from 08:00 h to 18:00 h. They were tested with data for a location in Portugal with data from 2014. Several tests were carried out with different combinations of inputs in order to arrive at the combination of inputs that had a smaller prediction error (). The optimal combination, for both models, includes all Numeric Weather Prediction variables, the average of the photovoltaic generation from the two days before and astronomical and time variables. The for this test is 9.22% and 7.68%, for the Day-ahead and Intraday models, respectively. The Intraday model proved to be more accurate than the Day-ahead model and both performed accurate forecasts in clear days and were less accurate in irregular days.Com o aumento da utilização das energias renováveis, é essencial ter mecanismos para ajudá-las a serem aceites social e tecnicamente. Um dos mecanismos que recentemente começou a ser utilizado é a previsão de geração renovável, nomeadamente da eólica e, neste caso, a fotovoltaica. Devido à natureza estocástica da geração fotovoltaica, ter uma boa previsão da geração futura ajuda os operadores da rede e os produtores individuais a gerir melhor as suas operações, aumentando assim a eficiência e a competitividade. Esta tese consiste em criar um algoritmo com a utilização de modelos de aprendizagem inteligente, na linguagem de programação R, para prever a geração de uma central fotovoltaica, na perspetiva do Operador de Distribuição. O modelo desenvolvido tem o objetivo final de ser uma ferramenta como suporte para a gestão da grade. Existem vários tipos de modelos de previsão, os quais: modelo de persistência, modelos físicos (sendo o mais conhecido denominado de Previsão Numérica do Tempo), modelos estatísticos (que se dividem em métodos regressivos e modelos de aprendizagem inteligente), e modelos híbridos (que se dividem em modelos híbridos estatísticos e modelos híbridos físicos). Sendo um dos objetivos desta tese a utilização de modelos de aprendizagem inteligente, teve-se em conta os seguintes modelos: redes neuronais, k-vizinhos mais próximos, máquinas de vetor suporte e florestas aleatórias. Após a avaliação de cada um, o modelo de florestas aleatórias foi o escolhido para desenvolver as previsões de geração fotovoltaica. As florestas aleatórias é um modelo que se baseia em árvores de decisão. Este tem como método o desenvolvimento de um grande número de árvores, todas elas independentes entre si, elaborar uma previsão com base no resultado de todas as unidades. Para além disso, as florestas aleatórias são ainda um modelo recente na previsão de geração fotovoltaica, pelo que é interessante avaliar o modelo e aprofunda-lo. Para além deste modelo, também foi escolhido o modelo de persistência. Este assume que a geração fotovoltaica na unidade de tempo é igual à geração em +1, sendo por isso o modelo de previsão mais simples e utilizado como linha de base quando comparado com outros modelos de previsão mais complexos. Os dados utilizados como entrada no modelo desenvolvido foram: dados históricos de prodição da central fotovoltaica em estudo, previsões meteorológicas, numa matriz 3x3 centrada na localização da central fotovoltaica, cedidas pelo Instituto Português do Mar e da Atmosfera (feitas através do modelo físico Previsão Numérica do Tempo), variáveis astronómicas, dia juliano e hora solar; todos eles relativos aos anos 2013 e 2014. As previsões meteorológicas consistem nas variáveis: velocidade do vento, direção do vento, radiação, temperatura, pressão, componente u e v do vento. Para avaliar a precisão da previsão, recorreu-se ao calculo do erro da previsão, que visa comparar a previsão dada pelo modelo e produção fotovoltaica real. Para isso utilizou-se o erro quadrado médio. Foi também calculado um modelo de céu limpo com o objetivo de auxiliar as previsões, na vertente de produção e de irradiação. Com esse modelo foi calculado o índice de céu limpo também para ambas as vertentes. Para tornar o modelo mais versátil e adequado às necessidades do Operador de Distribuição, foram criados dois modelos: um modelo Dia-seguinte e um modelo Intradiário. O modelo Dia-seguinte consiste numa previsão horária no início do dia e é a primeira visão geral quanto ao perfil de geração que a central fotovoltaica terá nesse dia. Em primeiro lugar calculou-se o valor da previsão, para 2014, através do modelo de persistência de duas formas: uma fazendo a média do valor da produção dos dois últimos dias à hora em que se quer prever e assumir que essa será a produção do dia seguinte e outra fazendo o mesmo procedimento, mas com o valor do índice de céu limpo. De seguida, o modelo de árvores aleatórias foi desenvolvido. Neste caso, utilizou-se os dados referentes a 2013 para treinar e validar o modelo e os de 2014 para testa-lo. As entradas do modelo variaram entre várias combinações dos dados acima referidos. Foram feitas várias análises com o objetivo de encontrar a combinação de dados que resultasse no menor erro de previsão, entre elas: avaliação das variáveis meteorológicas, astronómicas e de tempo; avaliação da importância das variáveis meteorológicas relativas ao vento, inclusão de previsões meteorológicas elaboradas um e dois dias anteriores, interpolação linear das variáveis, inclusão de dados meteorológicos de pontos vizinhos e inclusão de dados de produção passada. O erro de previsão da persistência foi superior à maioria dos testes elaborados pelas florestas aleatórias, com a exceção do teste que incluiu todas as variáveis meteorológicas com as astronómicas e as de tempo mais dados de produção passada produziu o melhor resultado. Os respetivos erros foram de 9.92% e 9.22%. Por outro lado, o modelo Intradiário tem o objetivo de ser realizado ao longo do dia, incluindo a última geração de PV para corrigir a previsão feita pelo modelo Dia-seguinte. Neste caso, o modelo de persistência foi o primeiro a ser calculado. Assumiu-se que o valor da produção fotovoltaica e do índice de céu limpo da hora anterior seria igual à hora seguinte. Quanto ao modelo de árvores aleatórias, teve-se em conta o melhor resultado do modelo Dia-seguinte, ou seja, manteve-se as mesmas variáveis de entrada e adicionou-se a geração fotovoltaica da hora anterior. Neste caso, o erro de previsão da persistência foi superior ao erro gerado pelo teste das florestas aleatórias. Sendo que o erro da persistência foi de 10.40% e o erro do modelo Intradiário de florestas aleatórias foi de 7.68%. Posto isto, conclui-se que o modelo Intradiário mostrou ser mais preciso do que o modelo Dia-seguinte. Por sim, foram escolhidos quatro dias do ano de 2014, um para cada estação do ano: outono, inverno, primavera e verão. Observou-se que em geral o modelo Intradiário seguiu o perfil da geração fotovoltaica real com um maior rigor que o Dia-seguinte, o que cumpre com as espectativas e com o objetivo inicial de o modelo Intradiário ser um ajuste ao longo do dia do modelo Dia-seguinte. Aferiu-se também que ambos os modelos são mais precisos em dias limpos e pouco irregulares. Quanto a dias com nuvens e irregulares, os modelos têm mais dificuldade em prever o dia ou a hora seguintes. Este trabalho demonstra que é possível elaborar previsões de produção fotovoltaica com base em previsões meteorológicas, dados passados de produção e variáveis facilmente calculáveis como a hora solar, o dia juliano, o azimute e a altura solar. Num futuro muito próximo será imprescindível para operadores da rede o acesso a modelos de previsão. A previsão de produção será tão necessária para esses agentes como a previsão meteorológica é para a comunidade em geral
Prediction in Photovoltaic Power by Neural Networks
The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches
Battery optimization in microgrids using Markov decision process integrated with load and solar forecasting
Rising climatic concerns call for unconventional/renewable energy sources which reduce the carbon footprint. Microgrids that integrate a variety of renewable energy resources play a key role in utilizing these energy resources in a more efficient and environmentally friendly manner. Battery systems effectively help to utilize these energy resources more efficiently. This research work presents a framework based on Markov Decision Process (MDP) integrated with load and solar forecasting to derive an optimal charging/discharging action of Battery with rolling horizon implementation. The load forecasting regression models are discussed and developed. Also, various solar forecasting models like clear sky, multi-regression and Non-Linear Autoregressive Neural Network model with Exogenous time-series are discussed and compared. The control algorithm is developed to reduce the monthly billing cost by reducing the peak load demand while also maintaining the state of charge of the battery. The presented work simulates the control algorithm for one month based on historic load and solar data. The results indicate substantial cost savings are possible with the proposed algorithm --Abstract, page iii
Deep Generative Models on 3D Representations: A Survey
Generative models, as an important family of statistical modeling, target
learning the observed data distribution via generating new instances. Along
with the rise of neural networks, deep generative models, such as variational
autoencoders (VAEs) and generative adversarial network (GANs), have made
tremendous progress in 2D image synthesis. Recently, researchers switch their
attentions from the 2D space to the 3D space considering that 3D data better
aligns with our physical world and hence enjoys great potential in practice.
However, unlike a 2D image, which owns an efficient representation (i.e., pixel
grid) by nature, representing 3D data could face far more challenges.
Concretely, we would expect an ideal 3D representation to be capable enough to
model shapes and appearances in details, and to be highly efficient so as to
model high-resolution data with fast speed and low memory cost. However,
existing 3D representations, such as point clouds, meshes, and recent neural
fields, usually fail to meet the above requirements simultaneously. In this
survey, we make a thorough review of the development of 3D generation,
including 3D shape generation and 3D-aware image synthesis, from the
perspectives of both algorithms and more importantly representations. We hope
that our discussion could help the community track the evolution of this field
and further spark some innovative ideas to advance this challenging task
Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation
We present a novel alignment-before-generation approach to tackle the
challenging task of generating general 3D shapes based on 2D images or texts.
Directly learning a conditional generative model from images or texts to 3D
shapes is prone to producing inconsistent results with the conditions because
3D shapes have an additional dimension whose distribution significantly differs
from that of 2D images and texts. To bridge the domain gap among the three
modalities and facilitate multi-modal-conditioned 3D shape generation, we
explore representing 3D shapes in a shape-image-text-aligned space. Our
framework comprises two models: a Shape-Image-Text-Aligned Variational
Auto-Encoder (SITA-VAE) and a conditional Aligned Shape Latent Diffusion Model
(ASLDM). The former model encodes the 3D shapes into the shape latent space
aligned to the image and text and reconstructs the fine-grained 3D neural
fields corresponding to given shape embeddings via the transformer-based
decoder. The latter model learns a probabilistic mapping function from the
image or text space to the latent shape space. Our extensive experiments
demonstrate that our proposed approach can generate higher-quality and more
diverse 3D shapes that better semantically conform to the visual or textural
conditional inputs, validating the effectiveness of the
shape-image-text-aligned space for cross-modality 3D shape generation.Comment: 20 pages, 11 figure
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