A Hybrid Deep Learning Model with Evolutionary Algorithm for Short-Term Load Forecasting

Mamun, Abdullahil; Hoq, Muntasir; Hossain, Eklas; Bayındır, Ramazan

doi:10.1109/icrera47325.2019.8996550

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…Three main processes: selection, crossover, mutation make the characteristics of GA to be similar with human genetic operation. More specifically, initialization, fitness evaluation, checking termination criterion, crossover, selection and mutation are the six dominant stages of GA that are shown by flow chart in Figure 4 following [20]. At the first stage, a chromosome in the desired search space is arbitrarily chosen.…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Data Driven Approach for Day Ahead Short Term Load Forecasting

Inteha

Masood

Hussain³

et al. 2022

IEEE Access

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This paper aims to develop an evolutionary deep learning based hybrid data driven approach for short term load forecasting (STLF) in the context of Bangladesh. With the lapse of time, the power system is getting complex. Penetration of intermittent renewable energy (RE) into the grid, changing prosumer load pattern with the need of demand side management (DSM) has thrown a challenge for dynamic power system operation and control. Load forecasting plays a significant role in this dynamic operation and control. In addition, it directly affects the future planning of network expansion, unit commitment and economic energy mix for power market. Day ahead short-term forecasting is very crucial for day to day operation. As such, various conventional and modified methods have been used over time for short-term prediction. Nevertheless, the existing approaches like age old statistical methods, artificial intelligence (AI), machine learning (ML), deep learning (DL) techniques alone cannot provide effective accuracy all the time. Hence, an integrated genetic algorithm (GA)-bidirectional gated recurrent unit (Bi-GRU) hybrid data driven technique (GA-BiGRU) is proposed in this work. The developed method is validated in Bangladesh power system (BPS) network by providing day ahead forecasting of electrical load of the whole country. Besides, the performance of the prediction model is compared with some existing approaches such as long short-term memory network (LSTM), gated recurrent unit (GRU) and integrated genetic algorithm-gated recurrent unit (GA-GRU) in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The outcome gives an indication of better forecasting accuracy of proposed GA-BiGRU evolutionary DL technique compared to others.INDEX TERMS Short-term load forecasting; Bidirectional gated recurrent unit; GA-BiGRU; Bangladesh power system; Genetic algorithm; Demand side management; MAPE; RMSE.

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Section: Genetic Algorithmmentioning

confidence: 99%

A Data Driven Approach for Day Ahead Short Term Load Forecasting

Inteha

Masood

Hussain³

et al. 2022

IEEE Access

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“…In recent years, deep-learning methods are becoming popular for demand forecasting. Thus, recurrent neural networks, with their variant LSTM and GRU, are the most widely used methodologies for forecasting, as can be found in [2,[7][8][9][10][11][12][13] for LSTM and [14,15] for GRU.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Residential Load Forecasting with Different Levels of Aggregation

Peñaloza

Leborgne

Balbinot

2022

The 8th International Conference on Time Series and Forecasting

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“…They applied feature selection process and Gaussian process regression for measuring the fitness score. A similar approach is discussed in [17], where authors used hybrid model of GA and LSTM. They used half-hourly data from the australian energy market operator.…”

Section: Related Workmentioning

confidence: 99%

Big Data Analytics for Energy Consumption Prediction in Smart Grid Using Genetic Algorithm and Long Short Term Memory

Kumari

Kumar

Rana

2021

cai

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Smart Grids (SG) have smart meters and advance metering infrasturutre (AMI) which generates huge data. This data can be used for predicting energy consumption using big data analytics. A very limited work has been carried out in the literature which shows the utilization of big data in energy consumption prediction. In this paper, the proposed method is based on Genetic Algorithm -Long Short Term Memory (GA-LSTM). LSTM memorises values over an arbitrary interval that manages time series data very effictively while GA is an evolutionary process that is used for optimization. GA combines with LSTM to process hyperparameters such as hidden layers, epochs, data intervals, batchsize and activation functions. Hence, GA creates a new vector for optimum solution that provides minimum error. These methods provide the best performance when compared with existing benchmarks. Moreover, GA-LSTM is used in a multi-threaded environment which increases the speed of convergence. Here, the multi-core platform is operated for solving one-dimensional GA-based inverse scattering problems. The result shows that GA-LSTM provides better convergence as compared to random approach techniques. For validating the results, Pennsylvania-New Jersey-Maryland Interconnection (PJM) energy consumption data has been used while adopting different performance evaluation metrics.

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A Hybrid Deep Learning Model with Evolutionary Algorithm for Short-Term Load Forecasting

Cited by 13 publications

References 20 publications

A Data Driven Approach for Day Ahead Short Term Load Forecasting

A Data Driven Approach for Day Ahead Short Term Load Forecasting

Comparative Analysis of Residential Load Forecasting with Different Levels of Aggregation

Big Data Analytics for Energy Consumption Prediction in Smart Grid Using Genetic Algorithm and Long Short Term Memory

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