Time Series Analysis of Electricity Consumption Forecasting Using ARIMA Model

Elsaraiti, Meftah; Ali, Gama; Musbah, Hmeda; Merabet, Adel; Little, Timothy

doi:10.1109/greentech48523.2021.00049

Cited by 37 publications

(20 citation statements)

References 15 publications

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“…Over the years, researchers have dedicated significant efforts to predicting energy consumption using a diverse range of ML/DL approaches. These studies have involved testing ML algorithms on datasets containing previously collected sensor data, including Support Vector Regressor (SVR) and Decision Tree Regressor (DTR) [13]. Artificial Neural Networks (ANN) have also been explored for their efficacy in predicting energy consumption trends.…”

Section: Review Of Ml/dl Modelsmentioning

confidence: 99%

A CNN and BiLSTM Fusion Approach Toward Precise Appliance Energy Forecasts

2024

IJIES

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Optimizing the energy grid management will enhance the effective and efficient use of generated energy. Accurate energy estimations allow power-generating firms to use dynamic energy management strategies to maintain stability across the smart grid. This paper presents a hybrid predictive modelling approach for forecasting the energy consumption of household appliances by combining 1D Convolutional Neural Networks (1D-CNN) with Bi-Directional Long Short-Term Memory (BiLSTM). The hybrid architecture combines CNNs' spatial feature extraction capabilities with BiLSTMs' sequential memory retention to comprehensively understand appliance use patterns. The proposed model is trained and validated on a dataset of household energy consumption, demonstrating superior performance compared to individual CNN or BiLSTM models. By incorporating both spatial and temporal data, energy consumption forecasts become more accurate and adaptable, making them highly appropriate for real-time applications and demand-side management. Utilizing the Analysis of Variance (ANOVA) F-measure feature selection technique and BiLSTM with a 1D-CNN hybrid deep learning model, the Root Mean Square Error (RMSE) of 1.745 and Coefficient of Determination (R 2 score) of 0.997.

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Section: Review Of Ml/dl Modelsmentioning

confidence: 99%

A CNN and BiLSTM Fusion Approach Toward Precise Appliance Energy Forecasts

2024

IJIES

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“…Konsumsi daya merupakan faktor yang sangat penting dalam smart grid untuk proses manajemen beban. Peramalan konsumsi energi adalah langkah pertama dalam menangani manajemen beban [7]. Untuk tempat yang dianalisis dalam jaringan, arus dihitung dari jatuh tegangan dan arus yang dihasilkan dari konsumsi listrik [8].…”

Section: Tinjauan Pustakaunclassified

Analisis Pemakaian Energi Listrik di Kantin Kampus 2 Universitas Pamulang

Kusnadi

Nurfadilah

Mardiansyah

et al. 2023

tech

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Energi listrik merupakan salah satu kebutuhan yang sangat penting untuk kehidupan serta dibutuhkan dalam berbagai kegiatan. Seiring perkembangan zaman kebutuhan listrik akan meningkat dengan adanya peningkatan dan perkembangan baik dari jumlah penduduk, jumlah investasi, perkembangan teknologi termasuk didalamnya perkembangan dunia pendidikan untuk semua jenjang pendidikan. Universitas Pamulang merupakan salah satu lembaga pendidikan yang memiliki kantin dengan konsumsi energi listrik cukup besar dengan total daya terpasang 24.700 VA (24,7 kVA) yang terbagi dalam 19 kios aktif dan 3 kios tidak aktif, dimana masing-masing kios memiliki daya 1.300 VA. Pada penelitian ini dilakukan analisis kapasitas, kebutuhan, dan biaya energi listrik di kantin belakang Kampus 2 UNPAM. Hasil dari penelitian ini memperlihatkan perbandingan rekening listrik dengan hasil analisis dari perhitungan beban, perhitungan energi dan perhitungan kapasitas daya terpakai. Hasilnya dapat dilihat hasil dari rekening listrik lebih besar dari pada hasil analisis, hal tersebut terjadi karena hasil analisis yang dihitung ideal sehingga tidak mengetahui pemakaian yang tidak terkontrol serta faktor dari jam nyala yang berlebihan sehingga menyebabkan pemakaian energi pada peralatan listrik menjadi besar dan adanya tambahan biaya beban dari pihak PLN persero.

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“…Currently, time series models can be roughly divided into three categories: classical statistical models, machine learning models, and hybrid models. The ARIMA model is one of the typical representatives of classical statistical models and has been widely used in load forecasting [4]. Commonly used machine learning models include the back propagation (BP) neural network [5], long short-term memory (LSTM) [6], and support vector network (SVM) [7].…”

Section: Introductionmentioning

confidence: 99%

Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting

Xiao,

Yang,

et al. 2024

PLoS ONE

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Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate the effectiveness of the proposed combination.

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Time Series Analysis of Electricity Consumption Forecasting Using ARIMA Model

Cited by 37 publications

References 15 publications

A CNN and BiLSTM Fusion Approach Toward Precise Appliance Energy Forecasts

A CNN and BiLSTM Fusion Approach Toward Precise Appliance Energy Forecasts

Analisis Pemakaian Energi Listrik di Kantin Kampus 2 Universitas Pamulang

Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting

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