Using the LSTM Network to Forecast the Demand for Electricity in Poland
Abstract
:1. Introduction
2. Energy Security
Poland’s Energy Policy
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- increasing energy efficiency by saving primary energy consumption by 13.6 Mtoe in 2010–2020 compared to the forecast of demand for fuels and energy from 2007,
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- increasing the share of energy from Renewable Energy Sources (RES) in gross final energy consumption to 15% by 2020,
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- contributing to the EU-wide reduction of greenhouse gas emissions by 20% (compared to 1990) by 2020 (in 2005 levels: −21% in the EU Emissions Trading System (EU ETS) sectors and −10% in the non-ETS).
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- reduction of greenhouse gas emissions by 40% compared to 1990 emissions (in terms of 2005 levels, i.e., a decrease of 43% in the EU ETS sectors and a decrease of 30% in non-ETS),
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- at least 32% share of renewable sources in gross final energy consumption,
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- increase in energy efficiency by 32.5%,
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- completing the internal EU energy market.
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- improving energy efficiency,
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- increased security of fuel and energy supplies,
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- diversification of the electricity generation structure by introducing nuclear energy,
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- development of the use of renewable energy sources, including biofuels,
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- development of competitive fuel and energy markets,
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- limiting the impact of the energy sector on the environment.
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- increasing the efficiency of electricity generation through the construction of highly efficient generation units,
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- a twofold increase in the production of electricity generated in the high-efficiency cogeneration technology by 2020, compared to production in 2006,
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- reduction of the network loss ratio in transmission and distribution, through inter alia, modernization of the existing networks and construction of new ones, replacement of low-efficiency transformers, and development of distributed generation,
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- increase in end-use energy efficiency,
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- increasing the ratio of the annual demand for electricity to the maximum demand for power at peak load, which allows reducing the total cost of meeting the demand for electricity [16].
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- setting a national target for increasing energy efficiency,
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- introducing a systemic support mechanism for activities aimed at achieving the national goal of increasing energy efficiency,
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- stimulating the development of cogeneration through support mechanisms, including cogeneration from sources below 1 MW, and the appropriate policy of municipalities,
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- applying mandatory energy performance certificates for buildings and apartments when placing them on the market and renting them,
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- marking the energy consumption of energy-consuming devices and products and introducing minimum standards for energy-consuming products,
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- commitment of the public sector to play an exemplary role in the efficient management of energy,
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- support for investments in the field of energy savings with the use of preferential loans and subsidies from national and European funds, including under the Act on supporting thermo-modernization and renovation, the Operational Program,
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- infrastructure and environment, regional operational programs,
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- funds from the National Fund for Environmental Protection and Water Management,
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- supporting scientific and research works in the field of new solutions and technologies reducing energy consumption in all directions of its processing and use,
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- the use of Demand Side Management techniques, stimulated by, e.g., daily diversification of distribution fees and electricity prices based on reference prices resulting from market introduction,
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- the current day and transferring price signals to recipients by means of remote two-way communication with electronic meters [16].
3. Electricity Production in Poland
4. Methods
5. Electricity Consumption on the Polish Market
6. Methodology of Electricity Final Consumption Forecasting
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- The study of the stability of the dispersion of the observation sequence was carried out using the F-Snedecor test for the variance determined from statistical samples.
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- The partial autocorrelation function plays an important role in data analysis to determine the effect of delay on the model. The procedure is to look for the point on the plot where the partial autocorrelations for all larger delays are essentially zero. For this purpose, the confidence interval is plotted on the graph. All values below the confidence interval are treated as zero partial autocorrelations. Partial autocorrelation values that are statistically significant were obtained for the delays of 1 and 3. A delay of 3, based on partial autocorrelation, was assumed.
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- Transformation-data standardization.
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- LSTM—training 200 steps, 25 epochs, the network parameters are shown in Table 1.
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- Analysis of obtained results based on test data—the analysis of the network learning mistakes.
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- Prediction of expired forecasts.
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- Statistical analysis of obtained results—an analysis of the generated results according to relationships 7 and 8.
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- Generating forecasts.
7. Conclusions
Funding
Conflicts of Interest
References
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Layer (Type) | Output Shape | Param |
---|---|---|
lstm_1 (LSTM) | (3.3) | 60 |
lstm_2 (LSTM) | (1) | 20 |
dense_3 (Dense) | (1) | 2 |
Total params: 82 | ||
Trainable params: 82 | ||
Non-trainable params: 0 |
Industry | Transport | Residential | Commercial and Public Services | Agriculture, Forestry, Fishing | |
---|---|---|---|---|---|
MAE (ktoe) | 54 | 6 | 20 | 38 | 8 |
RMSE (ktoe) | 97 | 11 | 45 | 60 | 23 |
MAPE (%) | 1 | 2 | 1 | 2 | 3 |
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Manowska, A. Using the LSTM Network to Forecast the Demand for Electricity in Poland. Appl. Sci. 2020, 10, 8455. https://doi.org/10.3390/app10238455
Manowska A. Using the LSTM Network to Forecast the Demand for Electricity in Poland. Applied Sciences. 2020; 10(23):8455. https://doi.org/10.3390/app10238455
Chicago/Turabian StyleManowska, Anna. 2020. "Using the LSTM Network to Forecast the Demand for Electricity in Poland" Applied Sciences 10, no. 23: 8455. https://doi.org/10.3390/app10238455