Energy consumption forecasting using ARIMA and neural network models

Nichiforov, Cristina; Stamatescu, Iulia; Făgărăşan, Ioana; Stâmâtescu, Grigore

doi:10.1109/iseee.2017.8170657

Cited by 58 publications

(18 citation statements)

References 10 publications

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“…Neural network is organized in form of layers having the predictors' or inputs' bottom layers, the forecasts' or outputs' top layer and intermediate layers containing "hidden neurons" [22]. Frequently used nonlinear autoregressive neural networks model NNAR(p, P, k)m [36] can be described by the following equation:…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Analysis of modern approaches for the prediction of electric energy consumption

et al. 2020

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AbstarctA review of modern methods of forming a mathematical model of power systems and the development of an intelligent information system for monitoring electricity consumption. The main disadvantages and advantages of the existing modeling approaches , as well as their applicability to the energy systems of Ukraine and Kazakhstan,are identified. The main factors that affect the dynamics of energy consumption are identified. A list of the main tasks that need to be implemented in order to develop algorithms for predicting electricity demand for various objects, industries and levels has been developed.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Analysis of modern approaches for the prediction of electric energy consumption

et al. 2020

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“…Yuan et al used GM (1, 1) model and ARIMA model to predict China's main energy consumption [36]. Cristina et al presented ARIMA model and autoregressive neural network (NAR) model for energy consumption forecast [37]. Abdollah et al proposed three forecasting models including autoregressive integrated moving average, the wavelet transform and artificial neural network, for short-term forecasting [38].…”

Section: Development Of Four Methodsmentioning

confidence: 99%

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

Wang

Zhan

2019

Sustainability

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Africa has abundant energy resources, but African energy research level is relatively low. In response to this gap, this paper takes Middle Africa as an example to systematically predict energy demand to give support. In this paper, we utilize four models, metabolic grey model (MGM), modified exponential curve method (MECM), autoregressive integrated moving average (ARIMA) and BP neural network model (BP), to predict the energy consumption of Middle Africa in the next 14 years. Comparing four completely different types of predictive models can fully depict the characteristics of the predictive data and give an all-round analysis of the predicted results. These proposed models are applied to simulate Middle Africa’s energy consumption between 1994 and 2016 to test their accuracy. Among them, the mean absolute percent error (MAPE) of MGM, MECM, ARIMA and BP are 2.41%, 4.80%, 1.91%, and 0.88%. The results show that MGM, MECM, ARIMA, and BP presented in this paper can produce reliable forecasting results. Therefore, the four models are used to forecast energy demand in the next 14 years (2017–2030). Forecasts show that energy demand of Middle Africa will continue to grow at a rate of about 5.37%.

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“…While defining the context of the current work, we also mentioned previous implementation that analyzed conventional system identification using Autoregressive Integrated Moving Average (ARIMA) models versus classical Artificial Neural Networks (ANN) [13]. Multiple ANN versions have been tested [14] in terms of number of hidden layers and number of neurons per layer.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

et al. 2019

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Buildings play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large-scale energy-management strategies from the supply side to the consumer side. When buildings integrate local renewable-energy generation in the form of renewable-energy resources, they become prosumers, and this adds more complexity to the operation of interconnected complex energy systems. A class of methods of modelling the energy-consumption patterns of the building have recently emerged as black-box input–output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produced by nondeterministic processes underlying energy consumption. We present an application of a class of neural networks, namely, deep-learning techniques for time-series sequence modelling, with the goal of accurate and reliable building energy-load forecasting. Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects, and are considered suitable for further use in future in situ energy management at the building and neighborhood levels.

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Energy consumption forecasting using ARIMA and neural network models

Cited by 58 publications

References 10 publications

Analysis of modern approaches for the prediction of electric energy consumption

Analysis of modern approaches for the prediction of electric energy consumption

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

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