Short-term power load forecasting with ordinary differential equation substitutions of polynomial networks

Zjavka, Ladislav; Snåšel, Václav

doi:10.1016/j.epsr.2016.04.003

Cited by 41 publications

(22 citation statements)

References 26 publications

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“…15 In this study, the demand forecast techniques are classified according to the planning horizon as very short-term, 16 short-term, 17 medium-term, 18 and long-term load forecasting. 15 In this study, the demand forecast techniques are classified according to the planning horizon as very short-term, 16 short-term, 17 medium-term, 18 and long-term load forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A comprehensive review about the most relevant studies on electric demand prediction over the last 40 years is discussed in a previous research. 15 In this study, the demand forecast techniques are classified according to the planning horizon as very short-term, 16 short-term, 17 medium-term, 18 and long-term load forecasting. 19 In models for mediumand long-term studies, such as DS expansion planning, it is common to represent the load in different levels.…”

Section: Literature Reviewmentioning

confidence: 99%

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Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

Neto

Abaide

Miranda

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper proposes a new probabilistic model for active low‐voltage prosumers suitable for distribution expansion planning studies. The load uncertainty of these consumers is considered through a range of load profiles by segmenting the energy consumption according to the different energy uses. Then, consumption adjustments are simulated using a nonhomogenous Poisson process based on the energy usage preferences and the financial gains according to the tariff scheme. A case study based on the modified IEEE 33‐Bus test system with real data collected from a Brazilian distribution company is performed in order to analyze the impact of the load profiles in scenarios with high penetration of renewable distributed generation (DG). The experiments carried out reveal that considerable monetary savings in the expansion of the distribution grid can be achieved for this case study (up to 37%) as compared with the alternative with no active demand (AD) by exploiting the flexibility associated with the active behavior of prosumers as a response to price signals and/or by permitting adequate levels for the integration of DG into the distribution grid.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

Neto

Abaide

Miranda

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…where Y i d is desired output, Y i is estimated output for ith training vector, M is number of data samples. The two initial gradually finishing iterations processes of the optimal two-input node blocks and neurons selection are performed simultaneously along with the permanent GSD updates to optimize the D-PNN structure, output PDE sum terms combination and polynomial parameters (Zjavka and Snášel, 2016). D-PNN with 20-40 inputs can build an optimal real data model using 6-8 layers.…”

Section: Appendix Appendix Amentioning

confidence: 99%

Local improvements in numerical forecasts of relative humidity using polynomial solutions of general differential equations

Zjavka

Sokol

2018

Quart J Royal Meteoro Soc

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Large‐scale forecast models are based on the numerical integration of differential equation systems, which can describe atmospheric processes in light of global meteorological observations. Mesoscale forecast systems need to define the initial and lateral boundary conditions, which may be carried out with robust global numerical models. Their overall solutions are able to describe the dynamic weather system on the Earth scale using a large number of complete globe 3D matrix variables in several atmospheric layers. Post‐processing methods using local measurements were developed in order to clarify surface weather details and adapt numerical weather prediction model outputs for local conditions. Differential polynomial network is a new type of neural network that can model local weather using spatial data observations to process forecasts of the input variables and revise the target 24 h prognosis. It defines and solves general partial differential equations, being able to describe unknown dynamic systems. The proposed forecast correction method uses a differential network to estimate the optimal numbers of training days and form derivative prediction models. It can improve final numerical forecasts, processed with additional data analysis and statistical techniques, in the majority of cases. The two‐stage procedure presented is analogous to the perfect‐prognosis method using real observations to derive a model, which is applied to the forecasts of the predictors to calculate output predictions.

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“…Consequently, some discontinuities in the electrical plant may occur due to the challenges accounting for the meteorological conditions such as the time of day, electricity cost, the population and social activity, etc. that (Zjavka and Snášel, 2016).…”

Section: Introductionmentioning

confidence: 99%

Electrical Load Forecasting Using Artificial Neural Network: The Case Study of the Grid Inter-Connected Network of Benin Electricity Community (CEB)

Salami¹,

Ajavon²,

Dotche³

et al. 2018

American Journal of Engineering and Applied Sciences

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Abstract:The low rate of electrification seems challenging in many West African countries and many strategies are underway to improve upon. In this regard, the target of achieving the universal access and services calls for a stable and reliable electrical network. Forecasting of electrical load on a connected grid network is very delicate and requires tremendous task from the utilities (billing Company). It aims at looking at if the offered energy is sufficient or below satisfactory in order to add or inject more compensating energy units into the system. Consequently, the short term forecasting is used in evaluating the risk of electricity shortage and reducing the advent of load shedding in an emerging economy alike the energetic Body of Benin comprising Togo and Benin. This paper evaluates two methods used in Artificial Neural Networks (ANN) for the prediction of electricity consumption. These methods are the Multilayer Perceptron (MLP) and the Radial Basic Function (RBF). Many topologies of the hidden layers' configuration for the learning stages were considered in cross comparison against real data obtained from the grid interconnected Network of Benin. The results have proven that the predicted data are very close to the real data while using these algorithms.

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Short-term power load forecasting with ordinary differential equation substitutions of polynomial networks

Cited by 41 publications

References 26 publications

Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

Local improvements in numerical forecasts of relative humidity using polynomial solutions of general differential equations

Electrical Load Forecasting Using Artificial Neural Network: The Case Study of the Grid Inter-Connected Network of Benin Electricity Community (CEB)

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