A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder

Yang, Mao; Zhao, Meng; Huang, Dawei; Su, Xin

doi:10.1016/j.renene.2022.05.141

Cited by 38 publications

(5 citation statements)

References 29 publications

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“…Step 1: the information of multi-location NWP is processed by RF-weighted feature extraction to assign weights, and the power values of autocorrelation and bias correlation tests are Step 2: for input samples X and Y, the conventional Euclidean distance lacks the description of the dynamic change characteristics of the time-series curve (Yang et al, 2022). Therefore, this study chooses the weighted distance between samples to be obtained by the following weighted clustering index calculation formula D E (X, Y), which results in the distance matrix D whose weighted distance index is shown as follows:…”

Section: An Error Scenario Partitioning Methods Considering Temporal ...mentioning

confidence: 99%

A new framework for short-term wind power probability forecasting considering spatial and temporal dependence of forecast errors

Sun

et al. 2022

Front. Energy Res.

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Since deterministic prediction errors of wind power cannot be avoided, probabilistic prediction can adequately describe the uncertainty of wind power and, thus, provide further guidance to dispatching authorities for decision making. Current probabilistic prediction methods for wind power are still incomplete in mining its physical variation process. Therefore, this study constructs a new framework for short-term wind power probabilistic forecasting considering the spatio-temporal dependence of errors by mining the spatio-temporal characteristics of historical wind power data and numerical weather forecasts at numerical weather prediction (NWP). First, the deterministic prediction results are obtained by an improved deep belief network (DBN); then, a multi-location NWP is introduced to propose a multi-level error scenario partitioning method considering the spatio-temporal dependence property. Finally, a new error sample set is formed by reconstructing the kernel density estimation method to adapt the model, and the short-term wind power probability prediction at different confidence levels is carried out. It is, thus, concluded that the effectiveness of the overall framework under the probabilistic prediction considering spatio-temporal dependence is verified in a wind farm in Jilin, China, and the prediction accuracy is effectively and significantly improved compared with the same confidence level, and the coverage of the evaluation index prediction interval is improved by 1.23, 0.72, and 0.80%, and the average bandwidth of the prediction interval is reduced by 2.14, 1.40, and 0.63%, which confirms the proposed effectiveness and feasibility of the method.

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Section: An Error Scenario Partitioning Methods Considering Temporal ...mentioning

confidence: 99%

A new framework for short-term wind power probability forecasting considering spatial and temporal dependence of forecast errors

Sun

et al. 2022

Front. Energy Res.

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“…A similarity measure is crucial in quantifying the degree of resemblance between two time series datasets. In this study, we employed dynamic time warping (DTW), a technique that has demonstrated significant efficacy in assessing similarity, particularly in the energy management sector [49]. DTW compares each point of one time series with multiple points of another, finding the best alignment by minimizing the cumulative distance between these matched points.…”

Section: Time Series Clusteringmentioning

confidence: 99%

A Novel Statistical Framework for Optimal Sizing of Grid-Connected Photovoltaic–Battery Systems for Peak Demand Reduction to Flatten Daily Load Profiles

Nematirad,

Pahwa,

Natarajan

2024

Solar

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Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model’s effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years.

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“…A similarity measure is crucial in quantifying the degree of resemblance between two time series datasets. In this study, we have employed Dynamic Time Warping (DTW), a technique that has demonstrated significant efficacy in assessing similarity, particularly in the energy management sector [47,48]. DTW compares each point of one time series with multiple points of another, finding the best alignment by minimizing the cumulative distance between these matched points.…”

Section: Time Series Clusteringmentioning

confidence: 99%

A Novel Statistical Framework for Optimal Sizing of Grid-Connected Photovoltaic-Battery Systems for Peak Demand Reduction to Flatten Daily Load Profiles

Nematirad,

Pahwa,

Natarajan

2024

Preprint

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Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. In many instances, utility companies pay for both energy and peak demand. PV systems can effectively reduce energy consumption during their operational periods, while, their contribution during peak hours, particularly in the early morning and late evening, is often limited. To address this issue, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV-battery systems remains a challenge. This study proposes a novel statistical model to optimize PV-battery system sizing for enhanced peak demand reduction. The model aims to flatten 95% of peak demand days up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV-battery system successfully flattens 95% of daily peak demand up to 2000 kW, yielding a financial benefit of $812,648 over 20 years.

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A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder

Cited by 38 publications

References 29 publications

A new framework for short-term wind power probability forecasting considering spatial and temporal dependence of forecast errors

A new framework for short-term wind power probability forecasting considering spatial and temporal dependence of forecast errors

A Novel Statistical Framework for Optimal Sizing of Grid-Connected Photovoltaic–Battery Systems for Peak Demand Reduction to Flatten Daily Load Profiles

A Novel Statistical Framework for Optimal Sizing of Grid-Connected Photovoltaic-Battery Systems for Peak Demand Reduction to Flatten Daily Load Profiles

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