No abstract

No abstract

Finding suitable forecasting methods for an effective management of energy resources is of paramount importance for improving the efficiency in energy consumption and decreasing its impact on the environment. Natural gas is one of the main sources of electrical energy in Algeria and worldwide. To address this demand, this paper introduces a novel hybrid forecasting approach that resolves the two-stage method's deficiency, by designing a Multi Layered Perceptron (MLP) neural network as a nonlinear forecasting model. This model estimates the next day gas consumption profile and selects one of several local models to perform the forecast. The study focuses firstly on an analysis and clustering of natural gas daily consumption profiles, and secondly on building a comprehensive Long Short Term Memory (LSTM) recurrent models according to load behavior. The results are compared with four benchmark approaches: the MLP neural network approach, LSTM, seasonal time series with exogenous variables models and multiple linear regression models. Compared with these alternative approaches and their high dependence on historical loads, the proposed approach presents a new efficient functionality. It estimates the next day consumption profile, which leads to a significant improvement of the forecasting accuracy, especially for days with exceptional customers consumption behavior change.

Predictive Functional Control (PFC), belonging to the family of predictive control techniques, has been demonstrated as a powerful algorithm for controlling process plants. The input/output PFC formulation has been a particularly attractive paradigm for industrial processes, with a combination of simplicity and effectiveness. Though its use of a lag plus delay ARX/ARMAX model is justified in many applications, there exists a range of process types which may present difficulties, leading to chattering and/or instability. In this paper, instability of first order PFC is addressed, and solutions to handle higher order and difficult systems are proposed. The input/output PFC formulation is extended to cover the cases of internal models with zero and/or higher order pole dynamics in an ARX/ARMAX form, via a parallel and cascaded model decomposition. Finally, a generic form of PFC, based on elementary outputs, is proposed to handle a wider range of higher order oscillatory and non-minimum phase systems. The range of solutions presented are supported by appropriate examples.

No abstract

No abstract

Recent developments in optical systems (isotope-selective non-dispersive infrared spectrometry) for breath testing have provided a robust, low-cost option for undertaking (13)C analysis. Although these systems were initially developed for breath testing for Helicobacter pylori, they have an enormous potential as a soil science research tool. The relatively low cost of the equipment, US$15,000-25,000, is within the research budgets of most institutes or universities. The simplicity of the mechanisms and optical nature mean that the equipment requires relatively low maintenance and minimal training. Thus methods were developed to prepare soil and plant materials for analysis using the breath test analyser. Results that compare conventional mass spectrometric methods with the breath test analyser will be presented. In combination with simple (13)C-plant-labeling techniques it is possible to devise methods for estimating carbon sequestration under different agronomic management practices within a short time frame. This enables assessment of the carbon credit value of a particular agronomic practice, which can in turn be used by policy makers for decision-making purposes. For global understanding of the effect of agricultural practices on the carbon cycle, data are required from a range of cropping systems and agro-ecological zones. The method and the approach described will enable collection of hard data within a reasonable time.

No abstract