def __init__(self,
encoder=None,
num_images=6,
time_interval_min=30,
dropout=0.20):
"""Initialize the architecture."""
super().__init__(NAME)
self.num_images = num_images
self.time_interval_min = time_interval_min
self.scaling_image = preprocessing.min_max_scaling_images()
self.scaling_ghi = preprocessing.min_max_scaling_ghi()
if encoder is None:
self.encoder = autoencoder.Encoder()
self.encoder.load(autoencoder.BEST_MODEL_WEIGHTS)
else:
self.encoder = encoder
self.flatten = layers.Flatten()
self.dropout = layers.Dropout(dropout)
self.gru1 = layers.GRU(1024, return_sequences=True)
self.gru2 = layers.GRU(512)
self.d1 = layers.Dense(512)
self.d2 = layers.Dense(256)
self.d3 = layers.Dense(128)
self.d4 = layers.Dense(4)
def __init__(self):
"""Initialize the architecture."""
super().__init__(NAME)
self.scaling_image = preprocessing.min_max_scaling_images()
self.scaling_ghi = preprocessing.min_max_scaling_ghi()
self.conv1 = self._convolution_step((5, 5), 64)
self.conv2 = self._convolution_step((3, 3), 128)
self.conv3 = self._convolution_step((3, 3), 256)
self.flatten = Flatten()
self.d1 = Dense(512, activation="relu")
self.d2 = Dense(256, activation="relu")
self.d3 = Dense(1)
def __init__(self, encoder: autoencoder.Encoder = None):
"""Initialize the architecture."""
super().__init__(NAME)
self.scaling_image = preprocessing.min_max_scaling_images()
self.scaling_ghi = preprocessing.min_max_scaling_ghi()
if encoder is None:
self.encoder = autoencoder.Encoder()
self.encoder.load(autoencoder.BEST_MODEL_WEIGHTS)
else:
self.encoder = encoder
self.flatten = Flatten()
self.d1 = Dense(512, activation="relu")
self.d2 = Dense(128, activation="relu")
self.d3 = Dense(1)
def __init__(self, num_images=4, time_interval_min=60):
"""Initialize the architecture."""
super().__init__(NAME)
self.scaling_image = preprocessing.min_max_scaling_images()
self.scaling_ghi = preprocessing.min_max_scaling_ghi()
self.num_images = num_images
self.time_interval_min = time_interval_min
self.inputdropout = layers.Dropout(0.5)
self.conv1a = layers.Conv3D(64, (3, 3, 3), padding="same")
self.pool1 = layers.MaxPooling3D(pool_size=(1, 2, 2), padding="same")
self.dropout1 = layers.Dropout(0.1)
self.batchnorm1 = layers.BatchNormalization()
self.conv2a = layers.Conv3D(128, (3, 3, 3), padding="same")
self.pool2 = layers.MaxPooling3D(pool_size=(2, 2, 2), padding="same")
self.dropout2 = layers.Dropout(0.1)
self.batchnorm2 = layers.BatchNormalization()
self.conv3a = layers.Conv3D(256, (3, 3, 3), padding="same")
self.conv3b = layers.Conv3D(256, (3, 3, 3), padding="same")
self.pool3 = layers.MaxPooling3D(pool_size=(2, 2, 2), padding="same")
self.dropout3 = layers.Dropout(0.1)
self.batchnorm3 = layers.BatchNormalization()
self.conv4a = layers.Conv3D(512, (3, 3, 3), padding="same")
self.conv4b = layers.Conv3D(512, (3, 3, 3), padding="same")
self.pool4 = layers.MaxPooling3D(pool_size=(2, 2, 2), padding="same")
self.dropout4 = layers.Dropout(0.1)
self.batchnorm4 = layers.BatchNormalization()
self.conv5a = layers.Conv3D(512, (3, 3, 3), padding="same")
self.conv5b = layers.Conv3D(512, (3, 3, 3), padding="same")
self.pool5 = layers.MaxPooling3D(pool_size=(2, 2, 2), padding="same")
self.dropout5 = layers.Dropout(0.1)
self.batchnorm5 = layers.BatchNormalization()
self.flatten = layers.Flatten()
self.d1 = layers.Dense(1048, activation="relu")
self.d2 = layers.Dense(521, activation="relu")
self.d3 = layers.Dense(256, activation="relu")
self.d4 = layers.Dense(256, activation="relu")
self.d5 = layers.Dense(4)
def __init__(
self,
encoder=None,
num_images=6,
time_interval_min=30,
dropout=0.25,
crop_size=64,
):
"""Initialize the architecture."""
super().__init__(NAME)
self.num_images = num_images
self.time_interval_min = time_interval_min
self.crop_size = crop_size
self.scaling_image = preprocessing.min_max_scaling_images()
self.scaling_ghi = preprocessing.min_max_scaling_ghi()
if encoder is None:
self.encoder = autoencoder.Encoder()
self.encoder.load(autoencoder.BEST_MODEL_WEIGHTS)
else:
self.encoder = encoder
self.flatten = layers.Flatten()
self.dropout = layers.Dropout(dropout)
self.conv1 = layers.Conv3D(
32, kernel_size=(3, 3, 3), padding="same", activation="relu"
)
self.conv2 = layers.Conv3D(
32, kernel_size=(3, 3, 3), padding="same", activation="relu"
)
self.conv3 = layers.Conv3D(
32, kernel_size=(3, 3, 3), padding="same", activation="relu"
)
self.conv4 = layers.Conv3D(
32, kernel_size=(3, 3, 3), padding="same", activation="relu"
)
self.max_pool = layers.MaxPooling3D((2, 2, 2))
self.d1 = layers.Dense(256)
self.d2 = layers.Dense(128)
self.d3 = layers.Dense(4)
def __init__(self):
"""Initialize the architecture."""
super().__init__(NAME_CLEARSKY_MATHE)
self.scaling_image = preprocessing.min_max_scaling_images()
self.scaling_ghi = preprocessing.min_max_scaling_ghi()
# Conv2D(channels, kernel_size=kernel_size, activation="relu")
self.conv1 = Conv2D(64, kernel_size=(3, 3), activation="relu")
self.conv2 = Conv2D(64, kernel_size=(3, 3), activation="relu")
self.dropout2 = Dropout(0.1)
self.maxpool1 = MaxPooling2D(pool_size=(2, 2))
self.conv3 = Conv2D(128, kernel_size=(3, 3), activation="relu")
self.conv4 = Conv2D(128, kernel_size=(3, 3), activation="relu")
self.conv5 = Conv2D(256, kernel_size=(3, 3), activation="relu")
self.conv6 = Conv2D(256, kernel_size=(3, 3), activation="relu")
self.maxpool2 = MaxPooling2D(pool_size=(2, 2))
self.flatten = Flatten()
self.d1 = Dense(1048, activation="relu")
self.d2 = Dense(512, activation="relu")
self.d3 = Dense(256, activation="relu")
self.d4 = Dense(256, activation="relu")
self.d5 = Dense(4)