A deep generative model for probabilistic energy forecasting in power systems: normalizing flows

Energy efficiency and consumers’ role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers’ power converters track the power setpoints obtained from the proposed strategy.

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A non-linear multivariable regression model for midterm energy forecasting of power systems

Electric Power Systems Research ◽

10.1016/j.epsr.2006.11.003 ◽

2007 ◽

Vol 77 (12) ◽

pp. 1560-1568 ◽

Cited By ~ 44

Author(s):

G.J. Tsekouras ◽

E.N. Dialynas ◽

N.D. Hatziargyriou ◽

S. Kavatza

Keyword(s):

Power Systems ◽

Regression Model ◽

Energy Forecasting ◽

Multivariable Regression Model ◽

Non Linear ◽

Multivariable Regression

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A WT-LUBE-PSO-CWC Wind Power Probabilistic Forecasting Model for Prediction Interval Construction and Seasonality Analysis

Energies ◽

10.3390/en14185942 ◽

2021 ◽

Vol 14 (18) ◽

pp. 5942

Author(s):

Ioannis K. Bazionis ◽

Markos A. Kousounadis-Knudsen ◽

Theodoros Konstantinou ◽

Pavlos S. Georgilakis

Keyword(s):

Power Systems ◽

Wind Power ◽

Upper Bound ◽

Probabilistic Models ◽

Prediction Interval ◽

Pso Algorithm ◽

Probabilistic Forecasting ◽

Energy Forecasting ◽

Proposed Model ◽

Artificial Neural Network Ann

Deterministic forecasting models have been used through the years to provide accurate predictive outputs in order to efficiently integrate wind power into power systems. However, such models do not provide information on the uncertainty of the prediction. Probabilistic models have been developed in order to present a wider image of a predictive outcome. This paper proposes the lower upper bound estimation (LUBE) method to directly construct the lower and upper bound of prediction intervals (PIs) via training an artificial neural network (ANN) with two outputs. To evaluate the PIs, the minimization of a coverage width criterion (CWC) cost function is proposed. A particle swarm optimization (PSO) algorithm along with a mutation operator is further implemented, in order to optimize the weights and biases of the neurons of the ANN. Furthermore, wavelet transform (WT) is adopted to decompose the input wind power data, in order to simplify the pre-processing of the data and improve the accuracy of the predictive results. The accuracy of the proposed model is researched from a seasonal perspective of the data. The application of the model on the publicly available data of the 2014 Global Energy Forecasting Competition shows that the proposed WT-LUBE-PSO-CWC forecasting technique outperforms the state-of-the-art methodology in important evaluation metrics.

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