scholarly journals Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1083 ◽  
Author(s):  
Óscar Trull ◽  
J. García-Díaz ◽  
Alicia Troncoso
Keyword(s):  
Demand Forecasting ◽  
State Space Model ◽  
Electricity Demand ◽  
Point Of View ◽  
Operating Conditions ◽  
A Posteriori ◽  
Proposed Model ◽  
External Variables ◽  
Discrete Interval ◽  
Box Cox Transformation

Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt–Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt–Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt–Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods.

scholarly journals Primary energy sources planning based on demand forecasting: The case of Turkey

2016 ◽  
Vol 27 (1) ◽  
pp. 2 ◽  
Author(s):  
Coşkun Hamzaçebi
Keyword(s):  
Demand Forecasting ◽  
Electricity Consumption ◽  
Primary Energy ◽  
Electricity Demand ◽  
Electricity Production ◽  
Energy Sources ◽  
Original Form ◽  
Grey Forecasting ◽  
Preliminary Information ◽  
Proposed Model

Forecasting electricity consumption is a very important issue for governments and electricity related foundations of public sector. Recently, Grey Modelling (GM (1,1)) has been used to forecast electricity demand successfully. GM (1,1) is useful when the observed data is limited, and it does not require any preliminary information about the data distribution. However, the original form of GM (1,1) needs some improvements in order to use for time series, which exhibit seasonality. In this study, a grey forecasting model which is called SGM (1,1) is proposed to give the forecasting ability to the basic form of GM(1,1) in order to overcome seasonality issues. The proposed model is then used to forecast the monthly electricity demand of Turkey between 2015 and 2020. Obtained forecasting values were used to plan the primary energy sources of electricity production. The findings of the study may guide the planning of future plant investments and maintenance operations in Turkey. Moreover, the method can also be applied to predict seasonal electricity demand of any other country.

Generic Modeling and Control of an Open-Circuit Piston Pump—Part I: Theoretical Model and Analysis

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Shu Wang
Keyword(s):  
Control Strategies ◽  
State Space Model ◽  
Open Circuit ◽  
Operating Conditions ◽  
Piston Pump ◽  
Generic Model ◽  
Parameter Uncertainties ◽  
Pump Design ◽  
Swash Plate ◽  
Proposed Model

Since variable-displacement open-circuits piston pumps are equipped with diverse compensators or controllers, many different modeling approaches and representations have been developed in the previous research. In the industry, the type of pump design (with an offset between the driving shaft and rotating center of the swash plate to neutralize the swash plate which replaces the bias piston) becomes more popular to reduce manufacturing costs that will be addressed in the research. To facilitate designs of electrohydraulic (EH) controllers and comparison studies of performance, the study proposes a generic state-space model of piston pumps acting in an open-circuit configuration by using generic regulator and unique reference inputs. One major contribution of the work is typical control strategies (including the pressure control, load-sensing control, and power control) in open-circuits pumps, which are described in one generic model. Thus, the model can be expediently used for investigations and improving piston pump designs. Even more important, the model can contribute as a unique and efficient plant to apply various model-based EH control that will be more convenient, intelligent, and less cost than current designs in the industry. Also, most previous modeling work of open-circuit piston pumps only concerns the steady-state results of the pump dynamics to simply the calculations that may ignore some important dynamics. The proposed model considers the high-order dynamics of the pump, such as swash plate velocity and accelerations. The variations caused by these terms are embedded in the model coefficients and regarded as the parameter uncertainties so that the model can take advantage of both modeling linearization and transient dynamics. It is highly challenging to analyze the stability and controllability issues during the design of piston pumps because they are impacted by many nonlinear parameters and operating conditions. So, the study presents another important methodology to analyze and define the critical design specification, such as stability, controllability, and observability. In the proposed model, the dynamical characteristics can be examined and compared by pumping subsystems and overall system in a single consistent platform. The controller gain scheduling and design performance are also able to assessed and determined while defining and specifying design criteria of the pump itself.

Do Event-Related Brain Potentials Reflect Mental (Cognitive) Operations?

2002 ◽  
Vol 16 (3) ◽  
pp. 129-149 ◽  
Author(s):  
Boris Kotchoubey
Keyword(s):  
Cognitive Processes ◽  
Point Of View ◽  
Internal Variables ◽  
Brain Potentials ◽  
Cognitive Operations ◽  
External Variables ◽  
Series Of Experiments ◽  
Mental Operations ◽  
A Chain ◽  
Processing Mechanisms

Abstract Most cognitive psychophysiological studies assume (1) that there is a chain of (partially overlapping) cognitive processes (processing stages, mechanisms, operators) leading from stimulus to response, and (2) that components of event-related brain potentials (ERPs) may be regarded as manifestations of these processing stages. What is usually discussed is which particular processing mechanisms are related to some particular component, but not whether such a relationship exists at all. Alternatively, from the point of view of noncognitive (e. g., “naturalistic”) theories of perception ERP components might be conceived of as correlates of extraction of the information from the experimental environment. In a series of experiments, the author attempted to separate these two accounts, i. e., internal variables like mental operations or cognitive parameters versus external variables like information content of stimulation. Whenever this separation could be performed, the latter factor proved to significantly affect ERP amplitudes, whereas the former did not. These data indicate that ERPs cannot be unequivocally linked to processing mechanisms postulated by cognitive models of perception. Therefore, they cannot be regarded as support for these models.

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