Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory

Li, Da; Sun, Kun; Huang, Han; Tang, Pingzhou

doi:10.3390/su10093282

Cited by 10 publications

(8 citation statements)

References 41 publications

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“…Dengan meningkatnya kegiatan ekonomi, kebutuhan listrik cenderung meningkat [9]. Untuk memprediksi tren permintaan seperti itu dengan akurasi yang lebih tinggi, penyertaan variabel ekonomi dalam model peramalan menjadi yang terpenting [10]. Selain itu, krisis ekonomi di suatu negara juga memiliki efek offset pada permintaan listriknya.…”

Section: Optimis DI Mana Kondisi Perekonomian Provinsiunclassified

Kebutuhan Energi Listrik Guna Menggerakkan Sektor Ekonomi Strategis di Provinsi Kalimantan Selatan

Putri

Nurhakim²,

Novianti³

et al. 2022

J. Geosapta

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Pemenuhan kebutuhan energi listrik di Provinsi Kalimantan Selatan diperkirakan tumbuh pesat, dengan adanya kawasan ekonomi khusus dan kawasan industri, sehingga diperlukan suplai energi listrik yang cukup besar. Kajian akan dihadapkan pada dua kondisi ekonomi, yaitu Business as Usual, dimana Provinsi Kalimantan Selatan masih mengalami pemulihan kondisi perekonomian yang merupakan dampak pandemi Covid-19, dan pertumbuhan ekonomi optimis, dimana Provinsi Kalimanatan Selatan mengalami percepatan ekonomi yang ditandai dengan terlaksananya rencana investasi proyek-proyek dalam kawasan ekonomi khusus dan kawasan industri. Metode yang digunakan dalam menentukan kebutuhan energi listrik adalah permodelan ekonometrika dengan pendekatan Ordinary Least Square (OLS). Model konsumsi energi listrik menggunakan variabel independent PDRB, jumlah penduduk, tarif rata-rata listrik, dan rasio elektrifikasi. PDRB dimodelkan menggunakan variabel independent jumlah tenaga kerja, pengeluaran pemerintah, dan investasi. Seluruh variabel menggunakan data historis dari tahun 2010 sampai dengan tahun 2021. Skenario 1 menghasilkan forecast PDRB dengan laju pertumbuhan rata-rata 2,72%.

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Section: Optimis DI Mana Kondisi Perekonomian Provinsiunclassified

Kebutuhan Energi Listrik Guna Menggerakkan Sektor Ekonomi Strategis di Provinsi Kalimantan Selatan

Putri

Nurhakim²,

Novianti³

et al. 2022

J. Geosapta

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“…The improvement of solutions related to short-term forecasting is also conducive to the development of currently necessary research on how to optimize the planning of complex energy storage systems for electric and gas vehicles [7]. The monthly and yearly load forecasting results are helpful, e.g., in renewable-energy integration processes, in mediumterm planning power plants or grids, and in generator maintenance scheduling [8]. On the other hand, LTLF is used primarily in long-term power system operation and planning, which can be based on macro-economic indicators (e.g., GDP and population), sectoral decomposition, technological penetration in various market segments and detailed temporal granularity [9].…”

Section: Introductionmentioning

confidence: 99%

Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models

Grzeszczyk

2022

Energies

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There is a lot of research on the neural models used for short-term load forecasting (STLF), which is crucial for improving the sustainable operation of energy systems with increasing technical, economic, and environmental requirements. Neural networks are computationally powerful; however, the lack of clear, readable and trustworthy justification of STLF obtained using such models is a serious problem that needs to be tackled. The article proposes an approach based on the local interpretable model-agnostic explanations (LIME) method that supports reliable premises justifying and explaining the forecasts. The use of the proposed approach makes it possible to improve the reliability of heuristic and experimental neural modeling processes, the results of which are difficult to interpret. Explaining the forecasting may facilitate the justification of the selection and the improvement of neural models for STLF, while contributing to a better understanding of the obtained results and broadening the knowledge and experience supporting the enhancement of energy systems security based on reliable forecasts and simplifying dispatch decisions.

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“…With increasing economic activity, the electricity demand tends to rise [4]. To predict such demand trends with a higher accuracy, the inclusion of economic variables in the forecasting models becomes paramount [5]. Therefore, while comparing such forecasting practices between Rising research trends in electricity load forecasting are primarily due to the integration of new technologies.…”

Section: Introductionmentioning

confidence: 99%

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

et al. 2020

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With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly.

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Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory

Cited by 10 publications

References 41 publications

Kebutuhan Energi Listrik Guna Menggerakkan Sektor Ekonomi Strategis di Provinsi Kalimantan Selatan

Kebutuhan Energi Listrik Guna Menggerakkan Sektor Ekonomi Strategis di Provinsi Kalimantan Selatan

Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

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