Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load

Jin, Bingjie; Zeng, Guihua; Lu, Zhe‐Ming; Peng, Hongqiao; Luo, Shu-Xin; Yang, Xiaoyong; Zhu, Hongbo; Liu, Mingbo

doi:10.3390/en15207584

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…As a result, the model achieved 92% accuracy without overfitting due to the robustness of the model, with more convolutional layers considered. Jin et al [17] attempted to predict the monthly peak demand for a city in China by developing a hybrid LSTM and backpropagation neural network (BPNN) scheme that separately extracts the relevant features related to peak demand, such as meteorological and economic indicators. The model is constantly updated by the BPNN, which continues to feed in future information to improve predictive performance.…”

Section: Literature Review and Research Gap Analysismentioning

confidence: 99%

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

et al. 2023

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The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%.

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Section: Literature Review and Research Gap Analysismentioning

confidence: 99%

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

et al. 2023

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“…Finally, for more works related to the load forecasting, refs. [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70] A dataset can be created to train the deep learning-based application (e.g., the ANN) to forecast the values of the loads. Then, based on Figure 4, the current values and the historical values of the desired inputs can be used to predict the future values of power consumption in a cyber-physical microgrid using the trained ANN.…”

Section: Deep Learning-based Load Forecastingmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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“…It is important to note that LSTM's effectiveness is demonstrated by its victory in the M4 forecasting competition of 2018, which employed 100,000 real-world time series [7]. Thus, the LSTM model and its variations [8][9][10][11][12][13][14][15], as well as their combinations with other forecasting models [16][17][18][19][20][21][22][23], are typically utilized for forecasting medium-term loads, as with other load forecasting timeframes.…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

Yaprakdal,

Varol Arısoy

2023

Applied Sciences

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In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operation. Although statistical methods were primarily used because ELF is a time series problem, deep learning (DL)-based forecasting approaches are more commonly employed and successful in achieving precise predictions. However, these DL-based techniques, known as black box models, lack interpretability. When interpreting the DL model, employing explainable artificial intelligence (XAI) yields significant advantages by extracting meaningful information from the DL model outputs and the causal relationships among various factors. On the contrary, precise load forecasting necessitates employing feature engineering to identify pertinent input features and determine optimal time lags. This research study strives to accomplish a mid-term forecast of ELF study load utilizing aggregated electrical load consumption data, while considering the aforementioned critical aspects. A hybrid framework for feature selection and extraction is proposed for electric load forecasting. Technical term abbreviations are explained upon first use. The feature selection phase employs a combination of filter, Pearson correlation (PC), embedded random forest regressor (RFR) and decision tree regressor (DTR) methods to determine the correlation and significance of each feature. In the feature extraction phase, we utilized a wrapper-based technique called recursive feature elimination cross-validation (RFECV) to eliminate redundant features. Multi-step-ahead time series forecasting is conducted utilizing three distinct long-short term memory (LSTM) models: basic LSTM, bi-directional LSTM (Bi-LSTM) and attention-based LSTM models to accurately predict electrical load consumption thirty days in advance. Through numerous studies, a reduction in forecasting errors of nearly 50% has been attained. Additionally, the local interpretable model-agnostic explanations (LIME) methodology, which is an explainable artificial intelligence (XAI) technique, is utilized for explaining the mid-term ELF model. As far as the authors are aware, XAI has not yet been implemented in mid-term aggregated energy forecasting studies utilizing the ELF method. Quantitative and detailed evaluations have been conducted, with the experimental results indicating that this comprehensive approach is entirely successful in forecasting multivariate mid-term loads.

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Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load

Cited by 6 publications

References 35 publications

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

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