Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine.

The electrical demand forecasting problem can be regarded as a non-linear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high resolution. To solve this challenging problem, various time series and machine learning approaches has been proposed in the literature. As an evolution of neural network-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by being stochastic and allowing bi-directional connections between neurons. In this paper, we investigate a newly developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for demand forecasting. The assessment is made on the EcoGrid EU dataset, consisting of aggregated electric power consumption, price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to real-time pricing signals. The results show that for the energy prediction problem solved here, FCRBM outperforms the benchmark machine learning approach, i.e. Support Vector Machine

Definitions of generalized multi-performance weighted multi-state K¯ -out-of- n system and its reliability evaluations

International audienceThe k-out-of-n system model is widely applied for the reliability evaluation of many technical systems. Multi-state system modelling is also widely used for representing real systems, whose components can have different levels of performance. For these researches, recently multi-state k-out-of-n systems have been comprehensively studied. In these studies, it is usually assumed that the system has a single task function to complete in a given environment. Moreover, the system or component performance is characterised by one measure, for example "electric power" in generation systems or "flow-rate" in transmission systems. However, this can be a simplification for some real-life engineering systems. For example, an intertwined district heating and electricity system consists of combined heat and power generating units, which can produce both electricity and heat. In this paper, definitions of multi-performance weighted multi-state components are provided and two generalized multi-performance multi-state K¯-out-of-n system models are proposed. Universal generating function approach is developed for the evaluation of such systems, with two numerical examples

Demand forecasting at low aggregation levels using factored conditional restricted Boltzmann machine

The electrical demand forecasting problem can be regarded as a non-linear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high resolution. To solve this challenging problem, various time series and machine learning approaches has been proposed in the literature. As an evolution of neural network-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by being stochastic and allowing bi-directional connections between neurons. In this paper, we investigate a newly developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for demand forecasting. The assessment is made on the EcoGrid EU dataset, consisting of aggregated electric power consumption, price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to real-time pricing signals. The results show that for the energy prediction problem solved here, FCRBM outperforms the benchmark machine learning approach, i.e. Support Vector Machine