Short Term Load Forecasting using ANN and Ensemble Models Considering Solar Irradiance

Vaish, Jayati; Datta, Stuti Shukla; Seethalekshmi, K.

doi:10.1109/ice348803.2020.9122986

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…This partition serves the purpose of refining the granularity of regional characteristics, specifically in response to the diverse meteorological conditions within the territorial expanse. The central challenge arises from the variability, primarily attributed to temperature fluctuations, which exerts a substantial impact on short-term demand patterns within hierarchical load forecasting [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Figueiró,

Abaide,

Neto

et al. 2023

Energies

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Activities related to the planning and operation of power systems use premise load forecasting, which is responsible for providing a load estimative for a given horizon that assists mainly in the operation of an electrical system. Hierarchical short-term load forecasting (STLF) becomes an approach used for this purpose, where the overall forecast is performed through system partition in smaller macro-regions and, soon after, is aggregated to compose a global forecast. In this context, this paper presents a bottom-up STLF approach for macro-regions. The main innovation is the Average Consumption per Meteorological Region (CERM) index, used to weigh the importance of each station meteorological (EM) in total load demand. Another index, the Variation of Load and Temperature (IVCT), based on historical temperature and demand changes, is proposed. These indexes are incorporated into an ANN model of the multi-layer perceptron type (MLP). The results showed a higher average performance of the index CERM and variable IVCT in relation to the other combinations performed, and the best results were used to compose the prediction of the MTR. Finally, the proposed model presented a Mean Absolute Percentage Error lower than 1%, presenting superior performance compared to an aggregate model for MTR, which shows the efficiency and contribution of the proposed methodology.

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Section: Introductionmentioning

confidence: 99%

Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Figueiró,

Abaide,

Neto

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

Weather classification-based load and solar insolation forecasting for residential applications with LSTM neural networks

Naware

Mitra

2021

Electr Eng

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A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.

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Short Term Load Forecasting using ANN and Ensemble Models Considering Solar Irradiance

Cited by 8 publications

References 19 publications

Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Weather classification-based load and solar insolation forecasting for residential applications with LSTM neural networks

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

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