DENCAST: distributed density-based clustering for multi-target regression

Corizzo, Roberto; Pio, Gianvito; Ceci, Michelangelo; Malerba, Donato

doi:10.1186/s40537-019-0207-2

Cited by 43 publications

(27 citation statements)

References 46 publications

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“…In the upper layer power allocation of the multi-microgrid system, the energy mutual aid problem between the microgrids is transformed according to (14)- (20) to solve the OPF problem. The actual power generation P s…”

Section: Consensus Algorithm Model Based On Equal Cost Increase Ratementioning

confidence: 99%

“…Finally, an islanded multi-microgrid system is used as an example to verify the effectiveness, reliability, and superiority of the proposed scheme. Aiming at the forecast output of the microgrids used in the paper, many studies have used the spatio-temporal aware approaches [19], prediction clusters [20] and neural networks [21,22] to study the forecast output of wind/solar, and have achieved good results. However, this paper focuses on the power allocation and economic scheduling of the multi-microgrid system; therefore, the forecast data involved are assumed to have been obtained.…”

Section: Introductionmentioning

confidence: 99%

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Bi-Level Optimal Strategy of Islanded Multi-Microgrid Systems Based on Optimal Power Flow and Consensus Algorithm

et al. 2020

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Aiming at problems of power allocation and economic scheduling for independent multi-microgrid systems, a bi-level optimization method based on optimal power flow and consensus algorithm is proposed. The novelty of the method is that an independent multi-microgrid system is divided into two layers: in the upper layer, with the predicted output range of the microgrids as the input data, each microgrid is considered as a virtual power supply or virtual load, and taking the minimum network loss as the goal, the energy mutual aid and power allocation among the microgrids are transformed into solving the optimal power flow; in the lower layer, taking the upper layer power distribution scheme as the constraint condition, considering load fluctuation and wind/solar generation uncertainty, the optimal dispatch model of the controllable distributed generator is established based on the distributed theory and the consensus algorithm of equal cost increment, and the "plug and play" of the distributed generator is also realized. An islanded multi-microgrid cluster is taken as an example to verify the economy, security, and reliability of the proposed scheme. The advantages of the scheme have been shown by the simulation example. Simulation results show that the upper-layer method not only realizes the optimal power allocation of microgrids, but also reduces the power loss of the energy mutual aid among the microgrids; through the optimal scheduling of controllable power supply in the microgrid, the lower-level scheme not only improves the economic benefit of the microgrid, but also well suppresses the negative effects of the uncertainties, prediction errors and power fault removal on the multi-microgrid system, which improves the robustness of the system.

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Section: Consensus Algorithm Model Based On Equal Cost Increase Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bi-Level Optimal Strategy of Islanded Multi-Microgrid Systems Based on Optimal Power Flow and Consensus Algorithm

et al. 2020

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“…In [18], a spatial-temporal forecasting method based on the vector autoregression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers, is presented. The general subject of time-series prediction is approached in [19] by proposing a distributed algorithm that performs density-based clustering and exploits the identified clusters to solve both single-and multi-target regression tasks. The same authors apply a method [20] that learns artificial neural networks to PV power forecasts.…”

Section: Of 17mentioning

confidence: 99%

Technical and Economic Optimal Solutions for Utility-Scale Solar Photovoltaic Parks

Silva

Castro

Batalha³

2020

Electronics

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Solar energy has shown to be a successful renewable source, wherefore large investments are planned in the upcoming decades. This work aims at developing technical and economical solutions to optimize a utility-scale grid connected solar photovoltaic park with an installed capacity of 24 MWp. Several successive simulations were automatically performed with the PVSyst software, so that each influencing parameter is individually analyzed. Moreover, a comprehensive study touching relevant aspects, such as central versus string inverters, landscape versus portrait dispositions, the optimal tilt angle (for fixed tilt systems), backtracking strategy (for single axis tracking systems), shading limit angle, and pitch is performed with the objective of finding the configuration leading to the higher grid injected energy output. The levelized cost of energy (LCOE) was obtained for 12 different cases in which, for both fixed tilt and single-axis tracking (SAT), central and string inverters and portrait/landscape disposition were considered. The lowest LCOE achieved was 32.23 €/MWh, which is a clear indication of the competitiveness of the solar Photovoltaic (PV) technology. The most economic viable solution is characterized by the employment of an SAT system with the backtracking strategy, together with string inverters and module portrait disposition.

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“…Liu et al [23] applied the stacked denoising auto-encoder (DAE) method to forecast electricity demand in a city in southern China. Moreover, distance-based predictive clustering [24,25] is used in time series forecasting.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting

et al. 2020

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As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.

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DENCAST: distributed density-based clustering for multi-target regression

Cited by 43 publications

References 46 publications

Bi-Level Optimal Strategy of Islanded Multi-Microgrid Systems Based on Optimal Power Flow and Consensus Algorithm

Bi-Level Optimal Strategy of Islanded Multi-Microgrid Systems Based on Optimal Power Flow and Consensus Algorithm

Technical and Economic Optimal Solutions for Utility-Scale Solar Photovoltaic Parks

Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting

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