An Improved Pattern Sequence-Based Energy Load Forecast Algorithm Based on Self-Organizing Maps and Artificial Neural Networks

Criado-Ramón, D.; Ruiz, L. G. B.; Pegalajar, M.C.

doi:10.3390/bdcc7020092

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…SOM is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional, discretized representation of the input space, often referred to as a map. The methodology for applying SOM in this study is as follows [50]:…”

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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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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show abstract

“…SOM is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional map. The methodology for applying SOM in this study is as follows [54]:…”

mentioning

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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Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption

Pérez-Chacón,

Asencio-Cortés,

Troncoso

et al. 2024

Future Generation Computer Systems

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An Improved Pattern Sequence-Based Energy Load Forecast Algorithm Based on Self-Organizing Maps and Artificial Neural Networks

Cited by 4 publications

References 23 publications

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

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

Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption

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