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Academic Journal of Energy, 2021, 2(2); doi: 10.38007/RE.2021.020202.

Structural Changes, Efficiency Improvements, and Energy Demand Forecasting Based on Empirical Analysis of Decomposition Models

Author(s)

Jalu Ahmad Prakosa

Corresponding Author:
Jalu Ahmad Prakosa
Affiliation(s)

Anna Univ, Chennai, Tamil Nadu, India

Abstract

Forecasting of energy demand plays a vital role in the built environment. The main purpose of this paper is to study the structural changes and efficiency improvements based on the empirical analysis of the decomposition model, and to analyze the related application research of energy demand forecasting. This paper mainly analyzes the impact of new energy participation on the electricity spot market, the forecast of new energy generation power and the forecast of electricity price in the electricity spot market. Experiments show that the first year to achieve the 18% emission reduction target is very likely. Under the baseline scenario, CI falls to 66.7% in the tenth year, implying that the reduction target of up to 65% is barely attainable. It can be concluded that if the industrial and energy structure in the tenth year remains unchanged compared to the first year, and the sectoral energy consumption follows the growth trend of the previous years, the CI can be reduced, but only slightly above the 65% setting Target.

Keywords

Decomposition Models, Empirical Analysis, Energy Demand, Demand Forecasting

Cite This Paper

Jalu Ahmad Prakosa. Structural Changes, Efficiency Improvements, and Energy Demand Forecasting Based on Empirical Analysis of Decomposition Models. Academic Journal of Energy (2021), Vol. 2, Issue 2: 10-20. https://doi.org/10.38007/RE.2021.020202.

References

[1] Ahmad T , Shah W A , Zhang D . Efficient Energy Planning with Decomposition-Based Evolutionary Neural Networks. IEEE Access, 2020, PP(99):1-1.

[2] Bot K , Ruano A , Ruano M G . Forecasting Electricity Demand in Households using MOGA-designed Artificial Neural Networks. IFAC-PapersOnLine, 2020, 53(2):8225-8230. https://doi.org/10.1016/j.ifacol.2020.12.1985

[3] Houchati M , Beitelmal M H , Khraisheh M . Predictive Modeling for Rooftop Solar Energy Throughput: A Machine Learning-Based Optimization for Building Energy Demand Scheduling. Journal of Energy Resources Technology, Transactions of the ASME, 2021, 144(1):1-15.

[4] Eseye A T , Lehtonen M . Short-term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models. IEEE Transactions on Industrial Informatics, 2020, PP(99):1-1.

[5] Williams S , Short M . Electricity Demand Forecasting for Decentralised Energy Management. Energy and Built Environment, 2020, 1( 2):178-186.

[6] Yadav H K , Pal Y , Tripathi M M . Short-term PV power forecasting using empirical mode decomposition in integration with back-propagation neural network. Journal of Information and Optimization Sciences, 2020, 41(1):25-37. https://doi.org/10.1080/02522667.2020.1714181

[7] Baker M A . Household Electricity Load Forecasting Toward Demand Response Program Using Data Mining Techniques in A Traditional Power Grid. International Journal of Energy Economics and Policy, 2021, 11(4):132-148. https://doi.org/10.32479/ijeep.11192

[8] Das P , Jha G , Lama A , et al. Empirical Mode Decomposition based Support Vector Regression for Agricultural Price Forecasting. Journal of Extension Education, 2020, 56(2):7-12.

[9] Bekirli A , Temurtas H , Zdemir D . Determination with Linear Form of Turkey's Energy Demand Forecasting by the Tree Seed Algorithm and the Modified Tree Seed Algorithm. Advances in Electrical and Computer Engineering, 2020, 20(2):27-34. https://doi.org/10.4316/AECE.2020.02004

[10] Mardiana S , Saragih F , Huseini M . Forecasting Gasoline Demand In Indonesia Using Time Series. International Journal of Energy Economics and Policy, 2020, 10(6):132-145. https://doi.org/10.32479/ijeep.9982

[11] Meenakshi, Mothi K . Forecasting Electricity Demand in Karnal city. Applied Ecology and Environmental Sciences, 2021, 9(7):707-714. https://doi.org/10.12691/aees-9-7-10

[12] Tufail M , Nawi M , Ali A , et al. Forecasting Impact Of Demand Side Management On Malaysia's Power Generation Using System Dynamic Approach. International Journal of Energy Economics and Policy, 2021, 11(4):412-418. https://doi.org/10.32479/ijeep.9716

[13] Schirmer P A , Geiger C , Mporas I . Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages. IEEE Access, 2021, PP(99):1-1.

[14] Maraj A . Modelling the Business-As-Usual Energy Scenario for the Albanian Household Sector. European Journal of Engineering Research and Science, 2020, 5(8):864-869. https://doi.org/10.24018/ejers.2020.5.8.2014

[15] Petrichenko L , Petrichenko R , Sauhats A , et al. Modelling the Future of the Baltic Energy Systems: A Green Scenario. Latvian Journal of Physics and Technical Sciences, 2021, 58(3):47-65. https://doi.org/10.2478/lpts-2021-0016

[16] Yousefi M , Hajizadeh A , Soltani M N , et al. Predictive Home Energy Management System With Photovoltaic Array, Heat Pump, and Plug-In Electric Vehicle. IEEE Transactions on Industrial Informatics, 2020, PP(99):1-1.

[17] Irl M , Lambert J , Wieland C , et al. Development of an Operational Planning Tool for Geothermal Plants With Heat and Power Production. Journal of Energy Resources Technology, 2020, 142(9):1-38. https://doi.org/10.1115/1.4047755

[18] Alaraj M , Kumar A , Alsaidan I , et al. Energy Production Forecasting from Solar Photovoltaic Plants based on Meteorological Parameters for Qassim Region, Saudi Arabia. IEEE Access, 2021, PP(99):1-1.

[19] Rafique S , Nizami M , Irshad U B , et al. EV Scheduling Framework for Peak Demand Management in LV Residential Networks. IEEE Systems Journal, 2021, PP(99):1-9.

[20] Jibran M , Nasir H A , Qureshi F A , et al. A Demand Response based solution to Overloading in Underdeveloped Distribution Networks. IEEE Transactions on Smart Grid, 2021, PP(99):1-1.