Research on Power Load Forecasting Method Based on LSTM Model

Cui, Can; He, Miao; Di, Fangchun; Lü, Yi; Dai, Yuhan; Lv, Fengyi

doi:10.1109/itoec49072.2020.9141684

Cited by 36 publications

(12 citation statements)

References 6 publications

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“…Utilizing the Keras library [5], the LSTM model on the other hand was developed by first [6]. Since LSTM is a variant of recurrent neural networks, the LSTM weights were initialized and subsequently used in the training of the neural network.…”

Section: Predictive Modelingmentioning

confidence: 99%

Optimization and simulation of a grid-connected PV system using load forecasting methods: A case study of a university building

Manlapaz,

Reyes,

Buensuceso

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Distributed generation represents a paradigm shift from the traditional electric grid to localized generation of electric power along with the capability of incorporating renewable energy (RE) sources into the energy mix. Responding to the need for sufficient analysis, simulation, and study of feasibility of distributed generation, this study aims to design a hybrid energy system for a university building and analyze its economic benefits. The viability of existing load forecasting methodologies for energy systems is also presented in this paper. The energy system design was determined through predictive modeling of the load profile of the building using historical data and optimization using machine learning methods, namely auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM). Simulations were run in HOMER Pro. Results show that a grid-connected solar photovoltaic (PV) system installed on the roof coupled with an energy storage system (ESS) will provide the most economic benefits because it yields a reduced cost of energy (COE) per kilowatt-hour (kWh) for the building. This study shows that in line with efforts to transition towards clean energy, hybrid energy systems using RE not only have economic benefits, but can also ensure energy security and environmental sustainability.

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Section: Predictive Modelingmentioning

confidence: 99%

Optimization and simulation of a grid-connected PV system using load forecasting methods: A case study of a university building

Manlapaz,

Reyes,

Buensuceso

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…There are many methods for load forecasting, and scholars have adopted a variety. Cui et al (2020) established the LSTM prediction model for load prediction to obtain more accurate power load prediction results according to the time series rule of power load [23]. Tian and Yao (2015) improved the subspace method by introducing the feedback factor and the forgetting factor, and then optimized the values of these factors by PSO algorithm to improve prediction accuracy [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China

Liu

Yin

2022

Energies

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In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling.

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“…e proposed method achieved superior forecasting performance in half-an-hour and one day-ahead compared with the existing method. Can et al utilized the LSTM to achieve the highest possible time-series power load accuracy for short-term prediction [36].…”

Section: Introductionmentioning

confidence: 99%

Three-Phase Load Prediction-Based Hybrid Convolution Neural Network Combined Bidirectional Long Short-Term Memory in Solar Power Plant

Lee

Thanh

Yeh

et al. 2022

International Transactions on Electrical Energy Systems

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e economic renewable energy generations have been rapidly developed because of the sharp reduction in the costs of solar panels. It is imperative to forecast the three-phase load power for more effective energy planning and optimization in a smart solar microgrid installed on a building in the Linyuan District, Taiwan. To alleviate this problem, this article proposes a convolution neural network bidirectional long short-term memory (CNN-Bi-LSTM) to accurately predict the short-term three-phase load power in building the energy management system in the smart solar microgrid with the collected data from advanced metering infrastructure (AMI), which have not been investigated before. e three-phase load-predicting methodology is developed using weather parameters and different collected data from AMI. e project evaluates the performance of the CNN-Bi-LSTM model by utilizing hyper-parameter optimization to attain the optimum parameters. e prediction models are trained based on hourly historical input features, selected based on the Pearson correlation coefficient. e performances' optimal structure CNN-Bi-LSTM are validated and compared with the bidirectional LSTM (Bi-LSTM), LSTM, the Gated Recurrent Unit (GRU), and the recurrent neural network (RNN) models. e obtained optimized structure of CNN-Bi-LSTM demonstrates the effectiveness of the proposed models in the short-term prediction of three-phase load power in a smart solar microgrid for building with a maximum enhancement of 68.36% and 8.81% average MSE, and 30.26% and 36.36% average MAE during the testing and validating operations.

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Research on Power Load Forecasting Method Based on LSTM Model

Cited by 36 publications

References 6 publications

Optimization and simulation of a grid-connected PV system using load forecasting methods: A case study of a university building

Optimization and simulation of a grid-connected PV system using load forecasting methods: A case study of a university building

Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China

Three-Phase Load Prediction-Based Hybrid Convolution Neural Network Combined Bidirectional Long Short-Term Memory in Solar Power Plant

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