Using the LSTM Network to Forecast the Demand for Electricity in Poland

Manowska, Anna

doi:10.3390/app10238455

Cited by 27 publications

(14 citation statements)

References 15 publications

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“…In addition, both the daily online sentiment data and the monthly car sales data were time series. Moreover, the LSTM network, which has a feedback loop for processing the entire data sequence, is always used for classification, processing, and forecasting based on time series data [27].…”

Section: The Cnn-lstm Modelmentioning

confidence: 99%

“…However, with the development of deep learning techniques, such as the Convolution Neural Networks (CNNs) and Long Short-Term Memory (LSTM), deep learning techniques have been recently applied to sales forecasting to improve the prediction performance [4,19,20,24,25]. The CNN is usually applied to image data for solving classification problems [26], while LSTM is used to analyze time series data for solving classification, processing, and forecasting problems [27].…”

Section: Introductionmentioning

confidence: 99%

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Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model

2022

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The automotive industry is the leading producer of machines in Taiwan and worldwide. Developing effective methods for forecasting car sales can allow car companies to arrange their production and sales plans. Capitalizing on the growth of social media and deep learning algorithms, this research aimed to improve the overall performance of the forecasting of Taiwan car sales movement direction forecasting by using online sentiment data and CNN-LSTM method. First, the historical sales volumes and multi-channel online sentiment data for six car brands in Taiwan were collected and preprocessed for labeling of car sales movement direction. Then, three models, namely, the classical, sentimental, and CNN-LSTM models, were constructed and trained/fitted for forecasting car sales movement directions in Taiwan. Finally, the performance of the three prediction models were compared to verify the effects of online sentiment data and the CNN-LSTM model on forecasting performance. The results showed that four forecasting performance indices, i.e., accuracy, precision, recall and F1-score, improved by 27.78% (from 41.67% to 69.45%), 0.39 (from 0.38 to 0.77), 0.27 (from 0.42 to 0.69) and 0.33 (from 0.35 to 0.68), respectively. Therefore, the online sentiment data and CNN-LSTM method can indeed improve the overall performance of car sales movement direction in Taiwan.

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Section: The Cnn-lstm Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model

2022

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“…The recoverable resources of the developed natural gas deposits amount to 95.81 billion m 3 , which constitutes 66.6% of the total amount of recoverable resources. The industrial resources of natural gas in 2020 amounted to 73.51 billion m 3 . From domestic raw materials, Poland covers only 20% of the demand.…”

Section: Gas Energy Minerals In Polandmentioning

confidence: 99%

“…The climate and energy policy of the European Union (EU), including the pursuit of EU climate neutrality by 2050, has a major impact on shaping the national energy strategy [1][2][3][4]. The regulatory package was adopted in 2009, setting three headline targets for tackling climate change by 2020 (3 × 20% package), in which member states participate according to their possibilities.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model

et al. 2021

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Natural gas is one of the main energy sources in Poland and accounts for about 15% of the primary energy consumed in the country. Poland covers only 1/5 of its demand from domestic deposits. The rest is imported from Russia, Germany, Norway, the Czech Republic, Ukraine, and Central Asia. An important issue concerning the market of energy resources is the question of the direct impact of the prices of energy resources on the income of exporting and importing countries. It should also be remembered that unexpected and large fluctuations are detrimental to both exporters and importers of primary fuels. The article analyzes natural gas deposits in Poland, raw material trade and proposes a model for forecasting the volume of its consumption, which takes into account historical consumption, prices of energy resources and assumptions of Poland’s energy policy until 2040. A hybrid model was built by combining ARIMA with LSTM artificial neural networks. The validity of the constructed model was assessed using ME, MAE, RMSE and MSE. The average percentage error is 2%, which means that the model largely represents the real gas consumption course. The obtained forecasts indicate an increase in consumption by 2040.

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“…With the continuous development of the neural network, Most scholars who study the electricity consumption prediction model recognize LSTM for its strong time-series learning ability and information selection ability. A. Jayanth Balaji et al [12][13][14][15] used the LSTM model to carry out related researches on electricity consumption and obtained the result of high prediction accuracy.…”

Section: Electricity Consumption Prediction Model Based On Neural Networkmentioning

confidence: 99%

Electricity Consumption Prediction Based on Time Series Data Features Integrate with Long Short-Term Memory Model

Wang

Mou

Lin

et al. 2021

Business Intelligence and Information Technology

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The forecast of electricity consumption is of great significance to adjusting the power supply dispatching scheme and optimizing economic structure. Electricity consumption prediction is a time series prediction problem in essence. Most relevant work considers establishing electricity consumption prediction models in terms of economy, temperature, region, etc. However, few studies consider the three data characteristics of trend, seasonality, and periodicity contained in time series. Therefore, this paper proposes a Long-Short Term Memory model based on time series feature fusion. This model considers the influence of three data features of time series on time prediction results and can effectively integrate time-series features into Long Short-Term Memory Model with strong self-learning ability. Experimental results show that the proposed method has higher accuracy than the basic LSTM model and the Autoregressive Integrated Moving Average Model (ARIMA).

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Using the LSTM Network to Forecast the Demand for Electricity in Poland

Cited by 27 publications

References 15 publications

Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model

Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model

Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model

Electricity Consumption Prediction Based on Time Series Data Features Integrate with Long Short-Term Memory Model

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