Electric load demand forecasting for Aborlan-Narra-Quezon distribution grid in Palawan using multiple linear regression

Supapo, Khrisydel Rhea M.; Santiago, Ronald Vincent M.; Pacis, Michael C.

doi:10.1109/hnicem.2017.8269480

Cited by 16 publications

(8 citation statements)

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“…where Y is the dependent variable, X 1 and X 2 are independent variables, βs are parameters to estimate, and e is the error term [13]. See Supapo et al [33] for a more detailed explanation of MLR. Even though it cannot capture nonlinear relationships by definition, MLR is used because of its scalability and interpretability, while also achieving state-of-the-art performances in many cases.…”

Section: Forecasting Models and Benchmarkmentioning

confidence: 99%

Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden

Netzell,

Kazmi,

Kyprianidis

2024

Energies

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As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the “weekly Naïve” benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.

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Section: Forecasting Models and Benchmarkmentioning

confidence: 99%

Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden

Netzell,

Kazmi,

Kyprianidis

2024

Energies

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“…Memanfaatkan data beban per jam terakhir dan data suhu, permintaan listrik jangka pendek untuk Pulau Sulawesi di Indonesia diperkirakan [13]. Di Filipina, beban listrik untuk jaringan diperkirakan menggunakan analisis regresi linier berganda [14]. Model mengambil data beban masa lalu dan rencana pengembangan masa depan sebagai variabel input [15].…”

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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“…Utilizing the past hourly load data and temperature data, the short term electricity demand for Sulawesi Island in Indonesia was forecasted [56]. In the Philippines, the electric load for a grid was forecasted using a multiple linear regression analysis [57]. The model takes past load data and future development plans as input variables.…”

Section: Regression Analysismentioning

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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Electric load demand forecasting for Aborlan-Narra-Quezon distribution grid in Palawan using multiple linear regression

Cited by 16 publications

References 0 publications

Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden

Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden

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

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

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