Direct short-term net load forecasting in renewable integrated microgrids using machine learning: A comparative assessment

Tziolis, Georgios; Lopez-Lorente, Javier; Baka, Maria-Iro; Koumis, Anastasios; Livera, Andreas; Theocharides, Spyros; Makrides, George; Georghiou, George E.

doi:10.1016/j.segan.2023.101256

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Other1

Preprint1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning

Sulaiman,

Jadin,

Mustaffa

et al. 2024

Journal of Building Engineering

View full text Add to dashboard Cite

Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning

Sulaiman,

Jadin,

Mustaffa

et al. 2024

Journal of Building Engineering

View full text Add to dashboard Cite

Research on optimization of improved short-term load composite forecasting model based on AM–CNN–Bi–LSTM

Zhao,

Ying,

et al. 2024

AIP Advances

View full text Add to dashboard Cite

Accurate load prediction is a prerequisite for the design, operation, scheduling, and management of energy systems. In the context of the development of smart grids, the extensive integration of highly volatile distributed energy generation into the power system has brought new challenges to the accuracy, reliability, real-time performance, and intelligence of short-term load forecasting. Therefore, this article proposes a novel short-term power load composite prediction model based on AM–CNN–Bi–LSTM. First, CNN is used to extract relevant feature quantities of power load coupling characteristics. Then, AM is used to evaluate the importance of the feature data, highlighting the features that have a greater impact on the prediction results. Finally, the Bi-LSTM network captures bidirectional temporal information from multiple time steps for prediction. Taking one year of measured data as an example, the error comparison of the prediction results of the composite prediction model overlay shows that compared with other models, the composite prediction model has improved prediction accuracy, feature extraction, generalization ability, and other aspects. The research results improve the accuracy of short-term power load forecasting while providing effective model references for decision-making in power system optimization scheduling, safe operation, and reasonable pricing.

show abstract

Comparison of Retraining Schedules in Ensemble Models for Enhanced Day-Ahead Electricity Net-Demand Predictions

Micallef,

Apap,

Licari

2024

2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)

View full text Add to dashboard Cite

Direct short-term net load forecasting in renewable integrated microgrids using machine learning: A comparative assessment

Cited by 4 publications

References 36 publications

Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning

Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning

Research on optimization of improved short-term load composite forecasting model based on AM–CNN–Bi–LSTM

Comparison of Retraining Schedules in Ensemble Models for Enhanced Day-Ahead Electricity Net-Demand Predictions

Contact Info

Product

Resources

About