Load Disaggregation is a broad term covering a range of techniques able to split a household’s energy consumption by the individual appliances used.
A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces. In this particular project i have used CNN based facial recognition system.
I used dataset from kaggle with some modification
Dataset structure
dataset has 31 classes of images of celebrity and there are some test data from internet
+-- face-recognition-dataset
| +-- Faces
| +-- Original Images
| +-- Dataset.csv(labes for images)
+-- test(images taken from internet)
Also I used ImageDataGenerator from tensorflow to ease the process of feeding data to CNN.
I was able to achieve 98% accuracy using this CNN architecture
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 222, 222, 32) 896
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 111, 111, 32) 0
_________________________________________________________________
batch_normalization (BatchNo (None, 111, 111, 32) 128
_________________________________________________________________
conv2d_1 (Conv2D) (None, 109, 109, 64) 18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 54, 54, 64) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 54, 54, 64) 256
_________________________________________________________________
conv2d_2 (Conv2D) (None, 52, 52, 64) 36928
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 26, 26, 64) 0
_________________________________________________________________
batch_normalization_2 (Batch (None, 26, 26, 64) 256
_________________________________________________________________
conv2d_3 (Conv2D) (None, 24, 24, 96) 55392
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 12, 12, 96) 0
_________________________________________________________________
batch_normalization_3 (Batch (None, 12, 12, 96) 384
_________________________________________________________________
conv2d_4 (Conv2D) (None, 10, 10, 32) 27680
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 5, 5, 32) 0
_________________________________________________________________
batch_normalization_4 (Batch (None, 5, 5, 32) 128
_________________________________________________________________
dropout (Dropout) (None, 5, 5, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 800) 0
_________________________________________________________________
dense (Dense) (None, 128) 102528
_________________________________________________________________
dense_1 (Dense) (None, 31) 3999
=================================================================
Time series forecasting have attracted a great deal of attention from various research communities. One of the method which improves accuracy of forecasting is time series clustering.
Here we are leveraging clustering since there are many customers and training a model for each is not scalable and efficient. For forecasting we are using fbprophet
plotly