Parameterizing residential water demand pulse models through smart meter readings

Topology-Based Estimation of Missing Smart Meter Readings

Smart meters often fail to measure or transmit the data they record when measuring energy consumption, known as meter readings, owing to faulty measuring equipment or unreliable communication modules. Existing studies do not address successive and non-periodical missing meter readings. This paper proposes a method whereby missing readings observed at a node are estimated by using circuit theory...

Water demand estimation and outlier detection from smart meter data using classification and Big Data methods

Automatic Meter Reading (AMR) systems are being deployed in many cities to obtain insight into the status and the behavior of District Metering Area (DMA) with more granularity. Until now, the water consumption readings of the population were taken one per month or one each two-months. In contrast, AMR systems provide hourly readings for households and more frequent readings for big consumers. ...

Hierarchical Probabilistic Forecasting of Electricity Demand with Smart Meter Data

Electricity smart meters record consumption, on a near real-time basis, at the level of individual commercial and residential properties. From this, a hierarchy can be constructed consisting of time series of demand at the smart meter level, and at various levels of aggregation, such as substations, cities and regions. Forecasts are needed at each level to support the efficient and reliable man...

The Use of Smart Meter Data to Forecast Electricity Demand

Over the past years, smart meters have been widely deployed in many countries. These devices replace conventional electrical meters and are able to provide measurements for time intervals of typically less than one hour and can send these to the utility. This technology provides utilities a large amount of data and the opportunity to use this data to improve the way they run the grid. We want t...

Parameterizing aerosol-water interactions