policies to achieve a good sustainable development. • Adding a new water source imply: • Higher costs. • Environmental damage. • Poorer quality. • “the largest, least expensive, and most environmentally sound source of water […] is the water currently being wasted in every sector of our economy”.  Gleick, P. et al. (2003). Waste Not, Want Not: The Potential for Urban Water Conservation
-> The 70% of total water consumption. • A good understanding of the demand and its characterization could be very useful to create good management policies. • Several problems can be addressed using AI techniques: • Final use classification (dishwasher, toilet, irrigation, taps). • Water demand forecasting.
device is very expensive and intrusive. • To overcome this problem, it is possible to install a unique precision meter at the home main water connection. • Predictive models can read these meters and make predictions: • End use: Classification problem. • Forecasting: Regression problem.
2008 a sample of 300 homes spread over the region of Madrid. • 15 million hours monitored for 9 years. • 35 million of events. • The sample is stratified and spread along different geographical areas of the region to be considered representative of the domestic users of Madrid. • The goal is the study of patterns of consumption and end uses of urban water.
End Uses of Water RESEARCH LINE Assurance of the balance (availability / demand) CLIENT Canal de Isabel II CONSORTIU M Exeleria: Preprocessing tasks Treelogic: Machine Learning tasks GOAL Developing an automatic system for identifying the end uses of water in the domestic applications, from the signals registered by water meters, using advanced techniques of machine learning, such as artificial neural networks (ANN) or other statistical methods
elementary unit of consumption that occurs in a period of time of enough duration, in which the instant flow can be clearly differentiated from the rest. • A particular domestic use may consist of one or more events. • One or several events that converge in time form an episode.
consist of more than one event, the events are overlapped. • Graphically the events are "stacked" on others as a ladder. • How do we discriminate events? o It is the same event if… ⁻ The flow rate keeps constant or the change is not significant. o It is a different event if… ⁻ There is a significant change in the flow rate.
major breakthrough in artificial intelligence with a high potential for predictive applications. • It has been recognized as one of ten breakthrough technologies according to MIT Technology Review. • DL has gone from being considered an academic field to being applied in engineering thanks to frameworks like TensorFlow or CNTK. • Very powerful, they can solve very complex tasks. • They require a large amount of data. • Large training times, they require specialized hardware for complex tasks. • Slow classifiers.
is that the training fast in the last layers (near the output), but very slow if we are far away from the output. • If we don’t have a lot of training data to perform a high number of back propagation iterations, we only train the layers at the output.. • If we can initialize the neural network with useful weights in the firsts layers, the training procedure will speed up. • If that initialization is not supervised we can use unlabeled data.
one hidden layer • With the same number of neurons in the input than in the output. • We add noise to the input and we train the network to recover the original input. • The network will learn to generalize because it will receive different data with the same output. • The network will learn to identify useful features of the image.
using autoencoders? • Stacking them. • We can remove the decoding layer and attach another autoencder in the output. • An autoencoder can just find basic useful weights. • The idea of autoencder in Deep Learning is using several autoencers training in a sequential way using the hidden layer as an input of the next autoencoder.
us to UNDERSTAND of the water demand and its characterization. Deep Learning Models can achieve very good results in terms of ACCURACY when is trained using large enough datasets. This METHODOLOGY is actually in use for processing data from the Panel for residential consumption patterns assessment and end- uses monitoring project of Canal de Isabel II in Madrid. It could be very USEFUL to create good management policies.