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WO2025017358A1 - Prédiction d'informations météorologiques à l'aide d'images provenant de caméras web - Google Patents

Prédiction d'informations météorologiques à l'aide d'images provenant de caméras web Download PDF

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Publication number
WO2025017358A1
WO2025017358A1 PCT/IB2023/057398 IB2023057398W WO2025017358A1 WO 2025017358 A1 WO2025017358 A1 WO 2025017358A1 IB 2023057398 W IB2023057398 W IB 2023057398W WO 2025017358 A1 WO2025017358 A1 WO 2025017358A1
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neural network
image
layer
data set
convolutional neural
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Roy SARKIS
Luisa LAMBERTINI
Demetri Psaltis
Christophe Moser
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Ecole Polytechnique Federale de Lausanne EPFL
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Ecole Polytechnique Federale de Lausanne EPFL
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/70—Labelling scene content, e.g. deriving syntactic or semantic representations

Definitions

  • the present invention relates to a method for predicting meteorological data or information, such as solar irradiance, using one or more webcam images processed by a novel neural network architecture combining a convolutional neural network (CNN) and a long short-term memory (LSTM).
  • the present invention provides an improved accuracy to predicting global horizontal solar irradiance (GHI) or other meteorological information.
  • GHI global horizontal solar irradiance
  • the invention aims to optimise the image fusion process by extracting meaningful features (such as clouds, shades, etc.) from the images while accounting for the temporal aspect of the data.
  • This invention may also benefit from feeding optional weather-related information, such as wind direction, humidity, and/or satellite images, to the neural network to further improve the prediction.
  • the present invention also relates to a prediction system configured to implement the method.
  • GHI prediction may be used for instance to better schedule and dispatch energy, and/or it may be used for storage planning and reserve activation.
  • PV photovoltaic
  • the collected images are then pre-processed to remove any distortion or noise and to extract relevant features, such as colour, texture or image channel ratios.
  • Cloud detection algorithms are then implemented to identify and classify the cloud coverage.
  • prediction models were developed to forecast GHI.
  • the models range from simple machine learning (ML) models, such as regression models, random forest, and support vector machines, to deep neural networks with multiple CNN and/or LSTM blocks.
  • ML machine learning
  • the developments of computer vision techniques and ML models enabled more precise GHI forecasts.
  • the main problem with satellite images is that they are low resolution images. Furthermore, both the satellite images and the all-sky cameras fail to show any cross-sectional view of the sky around the site of interest.
  • the methodology described in the present invention partially builds on the existing literature, but for instance it uses images from webcams (public or not) instead of all-sky cameras. Additionally, it optionally combines a plurality of streams of images from different locations.
  • An advantage of this methodology is the ease of scalability as it only requires the availability of webcams (which are already abundant) to collect the images. Hence, instead of having to install all-sky cameras in the sites of interest, one can simply tap into any surrounding webcam.
  • An additional benefit of the webcams is the cross- sectional view of the sky that they provide as opposed to all-sky or satellite cameras. This brings cloud height information that is not present in other types of images.
  • cross-sectional views contain a lot of information about the types of clouds as they show the different types of clouds at different elevations unlike the other types of images. Knowing the cloud type and at which elevation they are would allow their density as well as the speed at which they move to be determined because different types of clouds at different elevations in the atmosphere travel at different speeds.
  • Another advantage of the present invention relies on the optional combination of multiple image viewpoints as this improves the accuracy of the predictions. This can be achieved because the network is able to extract contemporaneous (i.e. , time-related) features from the different images that would help better understand the dynamics of the clouds surrounding the site of interest.
  • the present invention provides a new concept of image processing, optionally by fusing a plurality of images, using a deep neural network comprised of CNN and LSTM layers.
  • a deep neural network comprised of CNN and LSTM layers.
  • a computer program product comprising instructions for implementing the steps of the method when loaded and run on a computing apparatus.
  • a prediction system configured to implement the method according to the first aspect of the present invention.
  • Figure 1 schematically illustrates an example artificial deep neural network used to combine and analyse images to generate a GHI prediction according to an example of the present invention
  • Figure 2 schematically illustrates the concept of a time-distributed block
  • Figure 3 shows two sample images from two different webcams. These images may be used to train the proposed neural network or to generate a GHI prediction, for example;
  • Figure 4 shows three different images collected from an all-sky camera.
  • the three different images depict different weather conditions, namely starting from left to right: sunny, cloudy, and rainy;
  • Figure 5 illustrates one example network configuration for adding additional data to the existing deep neural network
  • Figures 6a and 6b show a flowchart illustrating the main steps of the process of performing a GHI prediction according to one example of the present invention.
  • “and/or” means any one or more of the items in the list joined by “and/or”.
  • “x and/or y” means any element of the three-element set ⁇ (x), (y), (x, y) ⁇ .
  • “x and/or y” means “one or both of x and y.”
  • “x, y, and/or z” means any element of the seven-element set ⁇ (x), (y), (z), (x, y), (x, z), (y, z), (x, y, z) ⁇ .
  • x, y and/or z means “one or more of x, y, and z.”
  • the term “comprise” is used herein as an open-ended term. This means that the object encompasses all the elements listed, but may also include additional, unnamed elements. Thus, the word “comprise” is interpreted by the broader meaning “include”, “contain” or “comprehend”. Identical or corresponding functional and structural elements which appear in the different drawings are assigned the same reference numerals. It is to be noted that the use of words “first”, “second” and “third”, etc. may not imply any kind of particular order or hierarchy unless this is explicitly or implicitly made clear in the context.
  • webcam refers to a digital camera that is primarily used to transmit real-time images designed for live streaming or video surveillance among other various applications. Webcams in the present context are thus configured to record video and/or capture still images.
  • a webcam and an “all-sky camera”. Whereas the latter is installed in open areas with a clear and unobstructed view of the sky (such as rooftops or weather stations), the former are installed almost anywhere. In this case, the interest is in webcams installed outdoors that capture the sky (a first part of the image) and the surrounding area (a second part of the image). Two example images captured by such webcams are shown in Figure 3. These images are panoramic images that capture images with horizontally elongated fields of view.
  • Figure 3 is 360-degree panoramic images, but other types of images are equally possible. As can be seen, one portion of these images shows the sky, and the remaining portion of these images shows the surrounding area (i.e. , elements other than the sky), such as the ground.
  • Figure 4 shows three images captured by an all-sky camera.
  • Webcams provide a better cross-sectional view of the sky compared to allsky cameras.
  • the cross-section is taken substantially along the direction of a surface normal of the ground.
  • additional information such as shadows and reflections, could be extracted from the images that could help predict GHL
  • images from all-sky cameras may experience some distortion due to the “fisheye lens” used to capture the entire hemisphere of the sky with a wide-angle perspective.
  • the distortion is usually corrected as modern all-sky cameras employ some corrective measures to ensure that the distortion is minimised. However, even with this correction, this could lead to some inaccuracies in the captured images.
  • the distortion could lead to geometric distortions, intensity variations of certain objects in the image or calibration errors.
  • the term “parameters” refers to the layers’ internal variables that are chosen by the user. These include but are not limited to the number of artificial neurons (also referred to as units or filters in some layers), the activation function and the kernel initialiser.
  • weights refers to the values of the connection between different neurons in the network. During training, their values are adjusted to optimise the network's performance.
  • bias refers to a constant that is added to a respective layer. The bias is also learned during the training process and is used for many reasons, such as for handling zero or near-zero inputs and shifting activation thresholds (this adds flexibility to the network to help it learn complex relationships in the data).
  • Conv2D refers to two-dimensional (2D) convolution.
  • 2D convolution the kernel (a matrix of a specific size) moves in two directions.
  • Input and output data of the 2D convolution are three-dimensional, which is typically the case with image data. This is in contrast with ConvI D.
  • 1 D convolution the kernel moves in one direction.
  • Input and output data of 1 D convolution are two-dimensional, which is the case with time-series data.
  • Input shapes of these two types of layers are supposed to be: Convl D: (size 1 , channel number), Conv2D: (size 1 , size 2, channel number).
  • RGB colour model images that have a height, width and depth (which are the channels).
  • LSM Long short-term memory
  • RNN recurrent neural network
  • Its relative insensitivity to gap length is its advantage over traditional RNNs, hidden Markov models and other sequence learning methods. It provides a short-term memory for RNN that can last thousands of timesteps, leading to its name long shortterm memory. It is especially applicable to classification, processing and predicting data based on time series.
  • the proposed neural network architecture 1 is schematically illustrated in Figure 1.
  • the first block of the proposed network 1 contains a first convolutional layer 3. Used as a layer for feature extraction and image recognition, the CNN algorithm takes an image 5 as input and extracts relevant features of the given image using filters (or kernels). The image is captured by a webcam, which is connected to the network 1 either wirelessly or by a wired connection.
  • the filters whose size may be specified by the user, are generally small square matrices that slide over the input image 5 to create feature maps. They slide across the input image. In this case they slide from the top left to the bottom right part of the input image. At every pixel of the image, a new value is computed based on the convolution operation.
  • the amount by which the filter moves horizontally and vertically in a given step is determined by the stride.
  • a stride of (2,2) for example means that the filter is moved 2 pixels horizontally and 2 pixels vertically in a given step.
  • a feature map is then created by moving the filter across the image (represented by a 3D matrix in our case) while performing the convolution operation at every step.
  • the edges, textures, or shapes of the input image are each represented by a distinct feature map.
  • the network may extract more intricate characteristics from the input image by layering a plurality of convolutional layers on top of one another.
  • a batch normalisation layer 7 is used before a second convolution layer 9.
  • a first pooling layer 10 and a first dropout layer 11 are then added to complete a first sub-block in a CNN block 13.
  • the pooling layer 10 is used to downsample the dimensionality in order to speed up calculations.
  • the pooling layer is a maxpooling layer.
  • the dropout layer 11 is used as a regularisation technique, which helps prevent overfittings as it allows the network to better generalise when testing.
  • the dropout layer simply drops a pre-specified fraction of neurons (by setting their output to zero). This means that the connection of these specific neurons is ignored during that particular training step.
  • the dropout is a stochastic process as the neurons dropped change from one training step to another. The dropping of random neurons will prevent the network from relying on particular nodes or from learning patterns and relationships present in the training data but not in the test data. With this layer, the sub-block in the CNN block 13 is completed.
  • the first sub-block is followed by a third convolutional layer 15, a second batch normalisation layer 17, a fourth convolutional layer 19, a second pooling layer 21 (which in this example is a maxpooling layer), and a second dropout layer 23.
  • the CNN block 13 is comprised of the following elements:
  • the first batch normalisation layer 7 is inserted between the two Conv2D layers 3, 9.
  • the batch normalisation layer normalises the output of the previous layer (in this case the previous Conv2D layer) before passing it to the next layer in contrast with normalising the network’s input data. This allows for higher learning rates and improves accuracy.
  • the first maxpooling layer 10 is introduced which reduces the spatial dimension of the input by selecting the maximum value for every pooling window. This leads to downsampling the feature maps and preserving the most prominent features.
  • the first dropout layer 11 is provided next.
  • this layer has a dropout rate of 0.3.
  • This layer acts as a regularisation technique to prevent overfitting as it randomly drops a fraction of its input which reduces the interdependencies between the neurons.
  • the CNN blocks 13 are wrapped under a time-distributed block 24. This indicates that the network is designed to process temporal sequences, in this case the sequence of images.
  • the time-distributed block 24 allows the same set of layers to be applied to multiple time steps of the input data. This extends the capabilities of the CNN to handle time series data.
  • One key aspect of the time-distributed block 24 is weight sharing. Some of the weights and preferably all the weights are shared across all the CNN blocks 13. This means that all the CNN blocks 13 have the same set of weights and biases.
  • Weight sharing allows the network 1 to learn and generalise temporal patterns and to extract similar features and patterns across different images. It also helps reduce the total number of parameters and hence, computational complexity.
  • each image has its dedicated CNN block 13.
  • the dashed arrows in Figure 2 represent the weights of the CNN blocks 13 that are shared between the upper and lower CNN blocks (one CNN block for each input).
  • the intuition is that the images 5 are taken at the same instant and have thus the same timestamp, and they share many common features.
  • sharing the CNN layers is not a problem as opposed to having two independent CNN blocks that are trained separately on each input image.
  • the network 1 comprises a flattening block or layer 27 for flattening the processed images from the CNN blocks 13, and a reshaping block or layer 29 for reshaping the data received from the flattening block.
  • the LSTM architecture or layer comprises a memory cell and three gates: input, output, and forget gates.
  • the input gate combines current input, previous output, and previous cell state using a sigmoid activation function.
  • the forget gate determines which information from the previous cell state is discarded based on the current input and previous output/state.
  • the output gate combines current input, previous output, and previous cell state.
  • the block output is computed by combining the current cell state and the output gate.
  • the first LSTM layer 31 is set to return sequences for each input time step rather than just a single output at the last time step. This sequence will then be the input for the second LSTM layer 32.
  • the first and second LSTM layers collectively form a LSTM architecture or block 33.
  • the output of the second LSTM layer 32 is fed into a fully connected dense layer 35 with 32 neurons in this case followed by a single-neuron dense layer 37 which acts as the output layer.
  • the output layer 37 outputs a real positive number representing the GHI.
  • the network is then trained with a set of images and an early stopping criterion of 10 epochs is added in this example. It should be noted that the “restore best weight” parameter is set to “True” to make sure to restore the model weights from the epoch with the lowest error.
  • the neural network is trained using the images from only two cameras as input and the GHI 2-hour ahead measurement as a label, i.e., as an output prediction.
  • images were collected from public webcams at regular intervals (for instance at intervals of a few seconds). This sampling allows the temporal changes to be observed in the cloud coverage as well as the sun's positioning throughout the day.
  • the example images are 360° images with a square shape of 250x250x3. Once all the images are collected, the timestamps between the two sets of images are cross-checked to ensure that the respective images used are taken at the same time.
  • the GHI measurements are collected such that at each timestamp, the 2- hour future GHI measurement is used.
  • the final dataset is then comprised of two images from two different cameras (taken at the same time) and a corresponding GHI measurement (two hours after the timestamp of the images).
  • the proposed neural network 1 depicted in Figure 1 serves only as an example to illustrate the idea of combining different images from a plurality of cameras (in this case from two cameras).
  • Including additional data can be achieved by adding one or more input layers and processing the new data separately before merging with the existing data.
  • Figure 5 presents a preview of adding new data. However, this represents merely one way of adding new data, but the invention is not limited to adding only one additional stream of new information.
  • adding a time series of meteorological data would mean adding a new input (a separate branch) to the network such that this data is not handled by the CNN blocks 13 but by another more appropriate layer, such as a concatenation layer 39 as shown in Figure 5.
  • the concatenation layer is configured to concatenate different data sets that are fed into this layer.
  • a first image 5 captured by a first webcam, and a second, different image 5 captured by a second, different webcam are fed into the deep neural network 1.
  • the first and second images which in this example are panoramic images, are taken at the same or substantially at the same time instant and have thus the same timestamp. Furthermore, the images taken by these cameras at least partially overlap, i.e., they show substantially the same surrounding but from a different angle. It is to be noted that the deep neural network 1 has previously been trained by using a training data set comprising training images captured by the first and second webcams.
  • step 62 first sets of convolutional layers, i.e., the first and second convolutional layers 3, 9 and the first batch normalisation layer 7 of the respective CNN block 13, are applied to the images to extract low- and mid-level features from the images.
  • step 63 the first pooling layers of the CNN blocks are applied to the outputs of the convolutional layers to extract the maximum output of the convolutional layers.
  • step 64 the first dropout layers of the CNN blocks are applied to the outputs of the first pooling layers to prevent the network from depending on particular neurons.
  • step 65 second sets of convolutional layers, i.e., the third and fourth convolutional layers 15, 19 and the second batch normalisation layer 17 of the respective CNN block 13, are applied to the outputs of the first dropout layers to extract mid- and high-level features from the images.
  • step 66 the second pooling layers 21 of the CNN blocks are applied to the outputs of the convolutional layers to extract the maximum output of the convolutional layers.
  • step 67 the second dropout layers of the CNN blocks are applied to the outputs of the second pooling layers to prevent the network from depending on particular neurons.
  • the first and second dropout layers have in this example mutually different dropout rates. More specifically, in this example, the dropout rate of the first dropout layers is greater than then dropout rate of the second dropout layers.
  • the memory block 33 which is able to extract time dependencies across different images captured at different time instants, processes the reshaped data set by considering the time dependencies to obtain a memory block output data set.
  • a respective time dependency reflects the difference between the present images and one image from the training data set (with an older timestamp).
  • the dense layer 35 which is formed by one or more regression-like layers, is applied to the memory block output data set to map the memory block output data set to the final output layer 37 to obtain a mapped data set.
  • the output layer 37 is applied to the mapped data set to obtain a predicted GHI value from the output layer.
  • some of the layers shown in Figure 1 may be optional depending on the shape and/or size of data matrices output by various layers.
  • the dense layer 35 may be optional.
  • the memory block would effectively map the reshaped data set to a suitable format.
  • one of the flattening and reshaping layers may also be optional.
  • the invention aims to improve the fusion process by extracting significant features from the images (such as clouds and shades) while considering the temporal aspect of the data.
  • significant features such as clouds and shades
  • the above teachings also apply to a scenario where a meteorological prediction is obtained only from one input image optionally complemented by additional meteorological data.

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Abstract

Un aspect de la présente invention concerne un procédé de prédiction d'informations météorologiques à l'aide d'un réseau de neurones profond artificiel. La présente invention, selon un exemple, se concentre sur un traitement d'image, en particulier sur la fusion d'une pluralité d'images obtenues par différentes caméras Web. Elle introduit une nouvelle architecture de réseau de neurones qui combine un réseau de neurones à convolution (CNN) et une mémoire à long et court terme (LSTM). Le but de cette fusion est d'améliorer la précision de prédiction d'informations météorologiques comparativement à l'utilisation d'une seule image. En tirant parti des capacités du CNN et de la LSTM, l'invention vise à améliorer le processus de fusion par l'extraction de caractéristiques significatives à partir des images (telles que des nuages et des ombres) tout en prenant en compte l'aspect temporel des données.
PCT/IB2023/057398 2023-07-20 2023-07-20 Prédiction d'informations météorologiques à l'aide d'images provenant de caméras web Pending WO2025017358A1 (fr)

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Citations (3)

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DE102017214875A1 (de) * 2017-08-25 2019-02-28 Robert Bosch Gmbh Verfahren zur Auswertung von Bilddaten, Computerprogramm und maschinenlesbares Speichermedium, sowie System zur Auswertung von Bilddaten
US20210004661A1 (en) * 2018-03-07 2021-01-07 Electricite De France Convolutional neural network for estimating a solar energy production indicator
US20210158010A1 (en) * 2018-05-31 2021-05-27 Siemens Aktiengesellschaft Solar irradiation prediction using deep learning with end-to-end training

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017214875A1 (de) * 2017-08-25 2019-02-28 Robert Bosch Gmbh Verfahren zur Auswertung von Bilddaten, Computerprogramm und maschinenlesbares Speichermedium, sowie System zur Auswertung von Bilddaten
US20210004661A1 (en) * 2018-03-07 2021-01-07 Electricite De France Convolutional neural network for estimating a solar energy production indicator
US20210158010A1 (en) * 2018-05-31 2021-05-27 Siemens Aktiengesellschaft Solar irradiation prediction using deep learning with end-to-end training

Non-Patent Citations (1)

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Title
HUERTAS-TATO JAVIER ET AL: "Using a Multi-view Convolutional Neural Network to monitor solar irradiance", NEURAL COMPUTING AND APPLICATIONS, SPRINGER LONDON, LONDON, vol. 34, no. 13, 21 April 2021 (2021-04-21), pages 10295 - 10307, XP037885356, ISSN: 0941-0643, [retrieved on 20210421], DOI: 10.1007/S00521-021-05959-Y *

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