Electricity demand and price forecasting model for sustainable smart grid using comprehensive long short term memory

Fatema, Israt; Kong, Xiaoying; Fang, Gengfa

doi:10.1080/19397038.2021.1951882

Cited by 14 publications

(9 citation statements)

References 58 publications

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“…These four weather measures are sometimes regarded as the most affectionate because they need the usage of air conditioners or electric heaters. Currency changes also affect cross-border electricity trade agreements and industrial costs [38,40].…”

Section: Simulation Resultsmentioning

confidence: 99%

A Study of Load Demand Forecasting Models in Electricity using Artificial Neural Networks and Fuzzy Logic Model

2022

IJE

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Since load time series are very changeable, demand forecasting of the short-term load is challenging based on hourly, daily, weekly, and monthly load forecast demand. As a result, the Turkish Electricity Transmission Company (TEA) load forecasting is proposed in this paper using artificial neural networks (ANN) and fuzzy logic (FL). Load forecasting enables utilities to purchase and generate electricity, load shift, and build infrastructure. A load forecast was classified into three sorts (hourly, weekly and monthly). Over time, forecasting power loads with artificial neural networks and fuzzy logic reveals a massive decrease in ANN and a progressive increase in FL from 24 to 168 hours. As illustrated, fuzzy logic and artificial neural networks outperform regression algorithms. This study has the highest growth and means absolute percentage error (MAPE) rates compared to FL and ANN. Although regression has the highest prediction growth rate, it is less precise than FL and ANN due to their lower MAPE percentage. Artificial Neural Networks and Fuzzy Logic are emerging technologies capable of forecasting and mitigating demand volatility. Future research can forecast various Turkish states using the same approach.

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Section: Simulation Resultsmentioning

confidence: 99%

A Study of Load Demand Forecasting Models in Electricity using Artificial Neural Networks and Fuzzy Logic Model

2022

IJE

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“…LSTM performance was better than SVM, regression tree (RT), and NARX. The results in [34][35][36] show an improvement in LSTM performance in the forecasting. However, time series data are used in prediction and the effect of other parameters in EPF is ignored.…”

Section: Introductionmentioning

confidence: 82%

“…The deep LSTM (DLSTM) has been used to predict the price and load of electricity with time series data in [35], and the superior performance of this network compared to the nonlinear autoregressive network with exogenous variables (NARX) and extreme learning machine (ELM) has been confirmed. In [36], using the Australian electricity market time series data, LSTM is used for EPF. LSTM performance was better than SVM, regression tree (RT), and NARX.…”

Section: Introductionmentioning

confidence: 99%

The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study

et al. 2022

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The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.

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“…According to the investigation, the factors affecting the electricity price are divided into four points, namely, the cost input of power generation, the historical electricity price, the power load, and others [4], content is as follows.…”

Section: Factors Affecting Electricity Pricementioning

confidence: 99%

Research and Application of Electricity Price Prediction Model Based on Big Data Analysis

Shi,

Zhang,

Wang

et al. 2024

Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December

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Based on the analysis of big data price prediction model, refers to the big data technology, on the basis of using big data technology analysis ability, effectively improve the price prediction model is scientific and logical, to get more accurate price forecast structure, and applied to the electric power enterprise, realize the purpose of improving electric power enterprise economic efficiency and power supply efficiency. Due to the large amount of information of electricity price data, there is no management between most samples and characteristics. In the input process, such redundant data may lead to the prediction of low accuracy prediction, and may increase the actual prediction time. Then, the research and application of the electricity price prediction model based on big data analysis has high research value and application significance. It can start from the basis of big data analysis and effectively improve the electricity price prediction model on the basis of big data analysis, so as to achieve the purpose of promoting the development of the power industry and enterprises.

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Electricity demand and price forecasting model for sustainable smart grid using comprehensive long short term memory

Cited by 14 publications

References 58 publications

A Study of Load Demand Forecasting Models in Electricity using Artificial Neural Networks and Fuzzy Logic Model

A Study of Load Demand Forecasting Models in Electricity using Artificial Neural Networks and Fuzzy Logic Model

The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study

Research and Application of Electricity Price Prediction Model Based on Big Data Analysis

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