A Multi-Valued Neuron Based Complex ELM Neural Network

Grasso, Francesco; Luchetta, Antonio; Manetti, S.

doi:10.1007/s11063-017-9745-9

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…Power quality disturbances could sometime hider the load forecasting capabilities; for this reason, specific classification techniques are often employed [32]. Due to the complexity of the forecasting problem, deep convolutional networks can often benefit from an automated definition of the hyperparameters by means of metaheuristic or evolutionary optimization algorithms [33,34], or networks trained through derivative-free optimization algorithms [35]. Forecasting of energy prices can be important to estimate future trends and optimize the economic aspects of a BESS [36][37][38].…”

Section: Machine Learning For Battery Energy Storage Systemsmentioning

confidence: 99%

Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

et al. 2021

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In this work, a strategy for scheduling a battery energy storage system (BESS) in a renewable energy community (REC) is proposed. RECs have been defined at EU level by the 2018/2001 Directive; some Member States transposition into national legislation defined RECs as virtual microgrids since they still use the existing low voltage local feeder and share the same low-medium voltage transformer. This work analyzes a REC which assets include PV generators, BESS and non-controllable loads, operating under the Italian legislative framework. A methodology is defined to optimize REC economic revenues and minimize the operation costs during the year. The proposed BESS control strategy is composed by three different modules: (i) a machine learning-based forecast algorithm that provides a 1-day-ahead projection for microgrid loads and PV generation, using historical dataset and weather forecasts; (ii) a mixed integer linear programming (MILP) algorithm that optimizes the BESS scheduling for minimal REC operating costs, taking into account electricity price, variable feed-in tariffs for PV generators, BESS costs and maximization of the self-consumption; (iii) a decision tree algorithm that works at the intra-hour level, with 1 min timestep and with real load and PV generation measurements adjusting the BESS scheduling in real time. Validation of the proposed strategy is performed on data acquired from a real small-scale REC set up with an Italian energy provider. A 10% average revenue increase could be obtained for the prosumer alone when compared to the non-optimized BESS usage scenario; such revenue increase is obtained by reducing the BESS usage by around 30% when compared to the unmanaged baseline scenario.

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Section: Machine Learning For Battery Energy Storage Systemsmentioning

confidence: 99%

Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

et al. 2021

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“…Liu et al 2019). The machine learning that has gained significant interest in the literature include NNs and its variants (Kumar, Singh, and Singh 2020;Ertuğrul 2018;Bansal et al 2019;Zou et al 2018;Lorencin et al 2019;Grasso, Luchetta, and Manetti 2018;Nayyeri et al 2018), support vector machine (SVM) (Bansal et al 2019), decision tree (DT) (Mantas et al 2019;Bansal et al 2019;Candanedo and Feldheim 2016), naïve Bayes (NB) (Bansal et al 2019), metaheuristics search algorithms and its variants (Bansal et al 2019;Aljarah, Faris, and Mirjalili 2018), random forest (RF) (Mantas et al 2019;Candanedo and Feldheim 2016), ensembles (Mantas et al 2019), gradient boosting machine (Candanedo and Feldheim 2016), regression and its variants (Lorencin et al 2019), and linear discriminant analysis (LDA) (Candanedo and Feldheim 2016).…”

Section: This Paper Proposes a Novel Cascade Principal Component Leas...mentioning

confidence: 99%

Cascade neural network algorithm with analytical connection weights determination for modelling operations and energy applications

Wang

Khan

et al. 2020

International Journal of Production Research

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The performance and learning speed of the Cascade Correlation neural network (CasCor) may not be optimal because of redundant hidden units' in the cascade architecture and the tuning of connection weights. This study explores the limitations of CasCor and its variants and proposes a novel constructive neural network (CNN). The basic idea is to compute the input connection weights by generating linearly independent hidden units from the orthogonal linear transformation, and the output connection weights by connecting hidden units in a linear relationship to the output units. The work is unique in that few attempts have been made to analytically determine the connection weights on both sides of the network. Experimental work on real energy application problems such as predicting powerplant electrical energy, predicting seismic hazards to prevent fatal accidents and reducing energy consumption by predicting building occupancy detection shows that analytically calculating the connection weights and generating non-redundant hidden units improves the convergence of the network.The proposed CNN is compared with that of the state-of-the-art machine learning algorithms. The work demonstrates that proposed CNN predicts a wide range of applications better than other methods.

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“…The proposed methodology was experimented on seven publicly available benchmark datasets available with the UCI Machine Learning Repository [ 50 – 52 ]. The seven datasets are Boston housing, Concrete Compressive strength, Airfoil self -noise, Istanbul Stock Exchange, Forest Fires, Abalone and Auto MPG.…”

Section: Particle Swarm Optimization: An Overviewmentioning

confidence: 99%

“…As can be seen, the proposed PSODS method is superior in terms of producing quality solutions compared to the results of all the other methods tabulated. The PSODS is superior in both training RMSE of 0.0780 and testing RMSE of 0.0844, as in both the cases the RMSE obtained is much less compared to other methods, followed by the DS method, as its testing RMSE is better compared to other networks [ 52 ].…”

Section: Particle Swarm Optimization: An Overviewmentioning

confidence: 99%

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

Victoire

2018

PLoS ONE

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This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.

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A Multi-Valued Neuron Based Complex ELM Neural Network

Cited by 14 publications

References 25 publications

Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

Cascade neural network algorithm with analytical connection weights determination for modelling operations and energy applications

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

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