Electric load forecasting based on Long-Short-Term-Memory network via simplex optimizer during COVID-19

Li, Xiaole; Wang, Yiqin; Ma, Guibo; Chen, Xin; Shen, Qianxiang; Yang, Bíngen

doi:10.1016/j.egyr.2022.03.051

Cited by 19 publications

(5 citation statements)

References 16 publications

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“…Improved Attention Mechanism. Due to the large number of features in the input photovoltaic prediction model, in order to highlight more critical factors and improve the accuracy of the model, this paper proposes an improved attention mechanism (IAM) based on the Competitive Random Search (CRS) algorithm [24,25]. It makes up for the deficiency that the network pays attention to the character-istics of different related factors on the same time scale and improves the accuracy of photovoltaic prediction models through the formulation of differentiated weight distributions.…”

Section: Iam-cnn-lstm Hybrid Neural Networkmentioning

confidence: 99%

An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

Zhao¹,

Khan

Akhtar

et al. 2022

International Journal of Photoenergy

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Solar energy is a costless and readily available form of energy that has shown to be one of the cleanest and most plentiful renewable energy sources. Various large-scale solar photovoltaic (PV) facilities are being utilized to minimize pollution and carbon emissions generated by fossil energy in many nations across the world. The power sequence of PV is influenced by a variety of diverse variables, and it is very unpredictable and volatile. Unlike the distributed PVs, the centralized PVs have the same intensity and location. The obstruction of clouds causes minor variations in the output power of the PV, making the power forecasting more difficult. To solve the aforementioned difficulties, this article provides a new neural network-based technique for PV power optimization and forecasting. The first stage is to create a cloud trajectory tracking system based on cloud photos taken from the ground. Second, a cloud trajectory tracking-based irradiance coefficient prediction model was built. Then, to increase forecast accuracy, build an error correcting model. For verification, data from a centralized solar power station was used. The results show that the proposed algorithm has technological applications and may greatly improve prediction accuracy.

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Section: Iam-cnn-lstm Hybrid Neural Networkmentioning

confidence: 99%

An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

Zhao¹,

Khan

Akhtar

et al. 2022

International Journal of Photoenergy

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show abstract

“…Although they have achieved desired performance in simple tasks under normal and stable conditions, their robustness declines dramatically when they are applied to power load forecasting, which involves non-linear and highly uncertain operation constraints (Kharin, 2013); By contrast, the machine learning-based forecasting algorithms are tasked to learning the non-linear mapping from exogenous information to the power load using historical data. For example, uses a fuzzy clustering approach to classify regional users and then builds respective random forest-based forecasting models for each class; Besides, (Li et al, 2021) and (Wang et al, 2020) make short-term load forecasts for heating load and electrical load, respectively, based on long short-term memory network (LSTM). However, the above three methods adopt equal weights for all the input features, which are correlated and exert different influences on power load.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Power Load Forecasting Under COVID-19 Based on Graph Representation Learning With Heterogeneous Features

Yang

et al. 2021

Front. Energy Res.

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Since 2020, the COVID-19 has spread globally at an extremely rapid rate. The epidemic, vaccination, and quarantine policies have profoundly changed economic development and human activities worldwide. As many countries start to resume economic activities aiming at a “living with COVID” new normal, a short-term load forecasting technique incorporating the epidemic’s effects is of great significance to both power system operation and a smooth transition. In this context, this paper proposes a novel short-term load forecasting method under COVID-19 based on graph representation learning with heterogeneous features. Unlike existing methods that fit power load data to time series, this study encodes heterogeneous features relevant to electricity consumption and epidemic status into a load graph so that not only the features at each time moment but also the inherent correlations between the features can be exploited; Then, a residual graph convolutional network (ResGCN) is constructed to fit the non-linear mappings from load graph to future loads. Besides, a graph concatenation method for parallel training is introduced to improve the learning efficiency. Using practical data in Houston, the annual, monthly, and daily effects of the crisis on power load are analyzed, which uncovers the strong correlation between the pandemic and the changes in regional electricity utilization. Moreover, the forecasting performance of the load graph-based ResGCN is validated by comparing with other representative methods. Its performance on MAPE and RMSE increased by 1.3264 and 15.03%, respectively. Codes related to all the simulations are available on https://github.com/YoungY6/ResGCN-for-Short-term-power-load-forecasting-under-COVID-19.

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“…Past studies have been able to categorize load forecasting based on projection and purpose which are classi ed into short, medium, and long-term forecasting [14,34]. Over the years, the interest of corporations in power or load demand forecast keeps growing which yields the demand for high-accuracy techniques for load demand forecasting by power rms, operators, and grids which also assist in detecting periods of low power demand to distribute power more e ciently and reliably.…”

Section: Introductionmentioning

confidence: 99%

High Precision Modeling of Power Demand for Electricity Distribution Companies with frequent load shedding

Oladeji

Fasogbon

2023

Preprint

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Globally, electricity forecast plays a critical role in ensuring the stability of the national grid. However, developing counties are challenged with a lack of data infrastructure for continuous data capture, storage, and process that ensure quality data are available for short-term power forecasting, a critical requirement for maintaining grid stability but at the same time utilizing loading scheduling for unbalanced power distribution and grid stability. This led to a huge gap and significant paucity in existing historical power demand. This research provides a mythological framework for modeling and missing value imputation of mean, median, and interpolation while combining meteorological data set from NASA and Power demand datasets from Distribution companies, an approach for improved short-term forecasting accuracy. This research considered Linear machine learning algorithms which include Linear regression, Lasso Linear Regression, Elastic Linear Regression, and nonlinear machine learning algorithms which are Support Vector Machine, Random Forest, Decision Tree, and K- Nearest Neighbor and deep learning algorithms like Artificial Neural Networks and Long Short Term Memory. Among these algorithms, the Deep learning method outperformed all of them with a performance accuracy of above 82.2% for ANN and with 91.2% performance which is prone to overfitting. Hyperparameter tuning with a batch size of 50 and an epoch of 5 gives the best performance for the deep learning methods.

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Electric load forecasting based on Long-Short-Term-Memory network via simplex optimizer during COVID-19

Cited by 19 publications

References 16 publications

An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

Short-Term Power Load Forecasting Under COVID-19 Based on Graph Representation Learning With Heterogeneous Features

High Precision Modeling of Power Demand for Electricity Distribution Companies with frequent load shedding

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