Time Series Analysis and Prediction of Electricity Consumption of Health Care Institution Using ARIMA Model

Kaur, Harveen; Ahuja, Sachin

doi:10.1007/978-981-10-3325-4_35

Cited by 28 publications

(28 citation statements)

References 10 publications

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“…The results of the experiments in Sections 3.2 and 3.3 are summarized as Table II, which shows that the proposed LSTM method with attention mechanism has the highest prediction 0.98 ARIMA [9] 0.53 SVM [11] 0.69 Neural Network (3 hidden layers) 0.70 Neural Network (4 hidden layers) 0.72 Neural Network (5 hidden layers) 0.71 LSTM [14] (without attention) 0.91 LSTM (with attention, proposed) 0.99 accuracy. Prediction accuracy increased by 6.5% compared to state-of-the-art model (LSTM without attention mechanism).…”

Section: Comparative Experimentsmentioning

confidence: 99%

Electricity consumption prediction based on LSTM with attention mechanism

Lin

Cheng

Huang

2020

IEEJ Transactions Elec Engng

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Power data analysis in power system, such as electricity consumption prediction, has always been the basis for the power department to adjust electricity price, substation regulation, total load prediction and peak avoidance management. In this paper, a short‐term time‐phased electricity consumption prediction model based on Long Short‐Term Memory (LSTM) with an attention mechanism is proposed. First, the attention mechanism is used to assign weight coefficients to the input sequence data. Then, the output value of every cell of LSTM is calculated according to the forward propagation method, and the error between the real value and the predicted value is calculated using the back‐propagation method. The gradient of each weight is calculated according to the corresponding error term, and the weight of the model is updated by the gradient descent direction to make the error smaller. Using modeling and predicting experiments on different types of electricity consumption, the results show that the prediction accuracy of the model proposed increased by 6.5% compared to the state‐of‐the‐art model. The model has a good effect on electricity consumption prediction. Not only can it be close to actual results numerically, but it can also better predict the development trend of data. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Comparative Experimentsmentioning

confidence: 99%

Electricity consumption prediction based on LSTM with attention mechanism

Lin

Cheng

Huang

2020

IEEJ Transactions Elec Engng

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“…Sağlık kurumlarında zaman serileri analizi ve elektrik tüketimi tahminini ARIMA modeli ile gerçekleştirmiştir. Çalışma da en uygun modelin bulunması ve aynı zamanda en uygun tahmin periyodunun da bulunması hedeflenmiştir [9]. Türkiye'nin toplam petrol talebi ve ulaştırma sektörü petrol talebinin ARIMA ile modellenmesi sağlanmış ve tahminleme gerçekleştirilmiştir.…”

Section: Teori̇k çErçeve Ve Hi̇potez (Therotical Framework and Hypothesis)unclassified

Talaşlı İmalat Sektöründe Zaman Serileri Kullanarak Üretim Etkililiğinin Tahmini

Aktaş¹,

Aydın

2018

Bilişim Teknolojileri Dergisi

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Günümüzde imalat sektöründe bulunan şirketlerin, üretim performansını, miktarını ve diğer değişkenleri tahmin edebilmesi rekabet ortamında rakiplerine ciddi bir avantaj sağlamalarına sebep olmaktadır. Üretim miktarlarının tahmin edilmesi tesisin gelecekteki giderlerini, üretim tezgâhlarındaki hata oranlarını ve karşılaşılacak krizlerin önceden tespit edilmesini sağlayarak verimlilik (etkililik) artışı sağlanmasına olanak tanımaktadır. Bu kapsamda talaşlı imalat sektöründe üretim performansının zaman serisi analizi kullanılarak yüksek doğrulukla ve hızlı bir şekilde tahmin edilmesi amaçlanmaktadır. Bunun için AR ve ARIMA metotları karşılaştırılarak uygun model seçimi gerçekleştirilmiştir. Seçilen model kullanılarak gerçeğe yakın tahmin değerlerine ulaşılmıştır. Modellenmiş üretim performansı değerinin üretim miktarı, üretim tonajı, üretilen parça başı ağırlık, kalıp revizyon süresi ve bakım arıza süresi değerlerinin her biri ile polinom regresyon yöntemi ile analizi yapılıp model oluşturulmuş ve performans tahmin değerinden yola çıkarak bu değerlerin her birisi için ileriye dönük tahmin edilmiştir. Bu modelin test MSE (Ortalama Karesel Hata), RMSE (Ortalama Karesel Hatanın Karekökü) ve MAPE (Ortalama Mutlak Hata yüzdesi) değerleri hesaplanmıştır. Elde edilen tahmini performans değerinden belirli ortalama hata payları ile tezgâhlarda üretilen adet sayısı, tonaj miktarı, bakım-arıza süresi, kalıp revizyon süresi, adet başı tonaj oranı gibi diğer verilerle ile ilgili tahminlerin yapılması sağlanmıştır.

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“…Latest studies show the intelligence of smart devices and sensor networks in improving the quality of people's life [1]. IoT devices are being used to collect data in order to analyze the behaviour and proper uses 5 of energy. Whereas energy consumption has always been an important issue as the number of devices using electrical power are increasing.…”

Section: Introductionmentioning

confidence: 99%

“…Different models have been used to predict power consumption like Autoregressive Integrated Moving Average model (ARIMA) [5], simple and multiple Linear Regression, Neuro-Fuzzy model [6], Support Vector Regression, Support Vector Machines (SVM) [7], Artficial Neural Network (ANN) [8], Time-Series 20 [9], or a combination of regression, Nearest Neighbor and ANN, whereas ANN has been considered to achieve the best results for energy prediction. Based on the statistics mentioned in [4], an overall of 47% of the energy consumption prediction models utilized ANN as machine learning algorithms, while 25% used SVM, 4% decision trees and 24% other statistical models.…”

Section: Introductionmentioning

confidence: 99%

An efficient data model for energy prediction using wireless sensors

Chammas¹,

Makhoul

Demerjian³

2019

Computers & Electrical Engineering

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Energy prediction is in high importance for smart homes and smart cities, since it helps reduce power consumption and provides better energy and cost savings.Many algorithms have been used for predicting energy consumption using data collected from Internet of Things (IoT) devices and wireless sensors. In this paper, we propose a system based on Multilayer Perceptron (MLP) to predict energy consumption of a building using collected information (e.g., light energy, day of the week, humidity, temperature, etc.) from a Wireless Sensor Network (WSN). We compare our system against four other classification algorithms, namely: Linear Regression (LR), Support Vector Machine (SVM), Gradient Boosting Machine (GBM) and Random Forest (RF). We achieve state-of-the-art results with 64% of the coefficient of Determination R 2 , 59.84% Root Mean Square Error (RMSE), 27.28% Mean Absolute Error (MAE) and 27.09% Mean Absolute Percentage Error (MAPE) in the testing set when using weather and temporal data.

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Time Series Analysis and Prediction of Electricity Consumption of Health Care Institution Using ARIMA Model

Cited by 28 publications

References 10 publications

Electricity consumption prediction based on LSTM with attention mechanism

Electricity consumption prediction based on LSTM with attention mechanism

Talaşlı İmalat Sektöründe Zaman Serileri Kullanarak Üretim Etkililiğinin Tahmini

An efficient data model for energy prediction using wireless sensors

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