AI-enabled metaheuristic optimization for predictive management of renewable energy production in smart grids

Sankarananth, S.; Karthiga, M.; E., Suganya; Sountharrajan, S.; Bavirisetti, Durga Prasad

doi:10.1016/j.egyr.2023.08.005

Cited by 25 publications

(3 citation statements)

References 28 publications

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“…Many literatures adopted neural networks to predict the output of renewable energy and loads, in order to reduce the impact of their volatility on the power system. Literatures [1]- [3] predicted the power of renewable energy and load with long short-term memory(LSTM). Literature [1] proposed a COA-CNN-LSTM algorithm to predict the power output of wind and photovoltaic.…”

Section: Introductionmentioning

confidence: 99%

“…Literature [2] showed that the accuracy of net load prediction was improved to 99.5% combined LSTM with fuzzy and discrete wavelet transforms. Literature [3] proposed a comprehensive approach combining LSTM with metaheuristic optimization algorithms for anticipating and managing renewable energy sources in smart grid environments. In order to make full use of the data of load, literature [4] predicted the long-term electricity demand forecast with adaptive neuro-fuzzy inference and verified the effectiveness of the algorithm by Ecuador.…”

Section: Introductionmentioning

confidence: 99%

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A reinforcement learning scheduling method for power system based on data-driven pretraining

Zhang,

Zhu,

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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With the fast development of renewable energy, a large amount of renewable energy is integrated into the power system. However, the intermittency and volatility of renewable energy sources such as solar and wind energy may pose huge challenges to power system scheduling. In order to reduce the impact of renewable energy volatility on the operation of the power system and improve the autonomy of the power system. This paper proposes a reinforcement learning scheduling method for power system based on data-driven pretraining. Firstly, it utilizes ge2e to encode wind, photovoltaic, and load, and performs data-driven pre training to obtain the embedding vectors of different sources；Then, the embedding vectors are used as the input features of the state, and the power system is scheduling based on the reinforcement learning method-SAC. This method is able to keep secure real-time scheduling of power system with highly volatile loads and renewable energy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A reinforcement learning scheduling method for power system based on data-driven pretraining

Zhang,

Zhu,

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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show abstract

“…By using decision trees, the authors in [25] have developed a process to improve the monitoring systems in smart buildings that help with energy distribution efficiency. The authors in [26] have proposed a comprehensive method to optimize the renewable energy production based on a hybrid LSTM-RL model in smart-grid application. The authors in [27] found that GRU is the most suitable to predict the output of wind turbine production compared with a statistical method.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings

Chakir,

Tabaa

2024

Sustainability

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Electricity demand in residential areas is generally met by the local low-voltage grid or, alternatively, the national grid, which produces electricity using thermal power stations based on conventional sources. These generators are holding back the revolution and the transition to a green planet, being unable to cope with climatic constraints. In the residential context, to ensure a smooth transition to an ecological green city, the idea of using alternative sources will offer the solution. These alternatives must be renewable and naturally available on the planet. This requires a generation that is very responsive to the constraints of the 21st century. However, these sources are intermittent and require a hybrid solution known as Hybrid Renewable Energy Systems (HRESs). To this end, we have designed a hybrid system based on PV-, wind-turbine- and grid-supported battery storage and an electric vehicle connected to a residential building. We proposed an energy management system based on nonlinear programming. This optimization was solved using sequential quadrature programming. The data were then processed using a long short-term memory (LSTM) model to predict, with the contribution and cooperation of each source, how to meet the energy needs of each home. The prediction was ensured with an accuracy of around 95%. These prediction results have been injected into K-nearest neighbors (KNN), random forest (RF) and gradient boost (GRU) repressors to predict the storage collaboration rates handled by the local battery and the electric vehicle. Results have shown an R2_score of 0.6953, 0.8381, and 0.739, respectively. This combination permitted an efficient prediction of the potential consumption from the grid with a value of an R²-score of around 0.9834 using LSTM. This methodology is effective in allowing us to know in advance the amount of energy of each source, storage, and excess grid injection and to propose the switching control of the hybrid architecture.

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Smart grid electricity theft prediction using cascaded R-CNN and hybrid metaheuristic optimization

Kumari,

Kumar,

Asoka

2024

Electr Eng

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AI-enabled metaheuristic optimization for predictive management of renewable energy production in smart grids

Cited by 25 publications

References 28 publications

A reinforcement learning scheduling method for power system based on data-driven pretraining

A reinforcement learning scheduling method for power system based on data-driven pretraining

Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings

Smart grid electricity theft prediction using cascaded R-CNN and hybrid metaheuristic optimization

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