Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection

Krstonijević, Sovjetka

doi:10.3390/s22197247

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…To handle unexpected occurrences or sudden changes in load, this integration allows for real-time decisionmaking, load-shedding, and resource allocation. Grid stability relies on the capacity to foresee and adjust to these changes [9].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach

Wang,

Nishtar

2024

Energies

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The transition to smart grids is revolutionizing the management and distribution of electrical energy. Nowadays, power systems must precisely estimate real-time loads and use adaptive regulation to operate in the era of sustainable energy. To address these issues, this paper presents a new approach—a hybrid neuro-fuzzy system—that combines neural networks with fuzzy logic. We use neural networks’ adaptability to describe complex load patterns and fuzzy logic’s interpretability to fine-tune control techniques in our approach. Our improved load forecasting system can now respond to changes in real-time due to the combination of these two powerful methodologies. Developing, training, and implementing the forecasting and control system are detailed in this article, which also explores the theoretical underpinnings of our hybrid neuro-fuzzy approach. We demonstrate how the technology improves grid stability and the accuracy of load forecasts by using adaptive control methods. Furthermore, comprehensive simulations confirm the proposed technology, showcasing its smooth integration with smart grid infrastructure. Better energy management is just the beginning of what our method can accomplish; it also paves the way for a more sustainable energy future that is easier on the planet and its inhabitants. In conclusion, this study’s innovative approach to adaptive control and real-time load forecasting advances smart grid technology, which, in turn, improves sustainability and energy efficiency.

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Section: Introductionmentioning

confidence: 99%

Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach

Wang,

Nishtar

2024

Energies

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“…Distinct forecasting methodologies are employed corresponding to the temporal horizon. While MTF and LTF forecasting often hinge upon trend analysis [2,3], end-use analysis [4], NN techniques [5][6][7], and multiple linear regressions [8], STF necessitates approaches such as regression [9], time series analysis [10], artificial NNs [11][12][13][14], expert systems [15], fuzzy logic [16,17], and support vector machines [18,19]. STF emerges as particularly critical for both transmission system operators (TSOs), guaranteeing the reliability of system operations during adverse weather conditions [20,21], and distribution system operators (DSOs), given the increasing impact of microgrids on aggregate load [22,23], along with the challenge of assimilating variable renewable energy sources to meet demand.…”

mentioning

confidence: 99%

Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network

Pavlatos,

Makris,

Fotis

et al. 2023

Electronics

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Precise anticipation of electrical demand holds crucial importance for the optimal operation of power systems and the effective management of energy markets within the domain of energy planning. This study builds on previous research focused on the application of artificial neural networks to achieve accurate electrical load forecasting. In this paper, an improved methodology is introduced, centering around bidirectional Long Short-Term Memory (LSTM) neural networks (NN). The primary aim of the proposed bidirectional LSTM network is to enhance predictive performance by capturing intricate temporal patterns and interdependencies within time series data. While conventional feed-forward neural networks are suitable for standalone data points, energy consumption data are characterized by sequential dependencies, necessitating the incorporation of memory-based concepts. The bidirectional LSTM model is designed to furnish the prediction framework with the capacity to assimilate and leverage information from both preceding and forthcoming time steps. This augmentation significantly bolsters predictive capabilities by encapsulating the contextual understanding of the data. Extensive testing of the bidirectional LSTM network is performed using multiple datasets, and the results demonstrate significant improvements in accuracy and predictive capabilities compared to the previous simpleRNN-based framework. The bidirectional LSTM successfully captures underlying patterns and dependencies in electrical load data, achieving superior performance as gauged by metrics such as root mean square error (RMSE) and mean absolute error (MAE). The proposed framework outperforms previous models, achieving a remarkable RMSE, attesting to its remarkable capacity to forecast impending load with precision. This extended study contributes to the field of electrical load prediction by leveraging bidirectional LSTM neural networks to enhance forecasting accuracy. Specifically, the BiLSTM’s MAE of 0.122 demonstrates remarkable accuracy, outperforming the RNN (0.163), LSTM (0.228), and GRU (0.165) by approximately 25%, 46%, and 26%, in the best variation of all networks, at the 24-h time step, while the BiLSTM’s RMSE of 0.022 is notably lower than that of the RNN (0.033), LSTM (0.055), and GRU (0.033), respectively. The findings highlight the significance of incorporating bidirectional memory and advanced neural network architectures for precise energy consumption prediction. The proposed bidirectional LSTM framework has the potential to facilitate more efficient energy planning and market management, supporting decision-making processes in power systems.

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Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection

Cited by 2 publications

References 37 publications

Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach

Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach

Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network

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