Oil and energy markets have experienced dramatic changes over the past three decades. Due to these changes, it may be difficult to model and forecast the oil consumption with traditional methods such as regression. Artificial Neural Networks (ANNs) are the strong rival of regression and time series in forecasting. ANNs provide good accuracy along with more reliable and precise forecasting for p...

The small medium large system (SMLsystem) is a house built at the Universidad CEU Cardenal Herrera (CEU-UCH) for participation in the Solar Decathlon 2013 competition. Several technologies have been integrated to reduce power consumption. One of these is a forecasting system based on artificial neural networks (ANNs), which is able to predict indoor temperature in the near future using captured...

We test for the presence of time-varying parameters (TVP) in the long-run dynamics of energy prices for oil, natural gas and coal, within a standard class of mean-reverting models. We also propose residual-based diagnostic tests and examine out-of-sample forecasts. In-sample LR tests support the TVP model for coal and gas but not for oil, though companion diagnostics suggest that the model is t...

We propose a simple empirical scaling law that describes load forecasting accuracy at different levels of aggregation. The model is justified based on a simple decomposition of individual consumption patterns. We show that for different forecasting methods and horizons, aggregating more customers improves the relative forecasting performance up to specific point. Beyond this point, no more impr...

Under the situation that single prediction can’t meet the accuracy requirements, combined forecasting method is usually the first choice. Using the history data of domestic oil products consumption, by optimized combination of grey forecasting GM(1, 1) model and artificial neural network, the combined forecasting model was built. It can be getting from the combined forecasting model that on the...

Segmentation of Residential Gas Consumers using Clustering Analysis Marta P. Fernandes 1,†,‡*, Joaquim L. Viegas 1,†,‡, Susana M. Vieira 1,†,‡ and João M. C. Sousa 1,†,‡ 1 IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Portugal; {marta.fernandes, joaquim.viegas, susana.vieira, jmsousa}@tecnico.ulisboa.pt * Correspondence: [email protected]; Tel.: +351-218-417-000 ‡ ...

Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level....

This work provides a preliminary study on applying state-of-the-art time-series forecasting methods to electrical energy consumption data recorded by smart metering equipment. We compare a custom-build commercial baseline method to modern ensemble-based methods from statistical time-series analysis and to a modern commercial GP system. Our preliminary results indicate that that modern ensemble-...