Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning

Ramos, Daniel; Faria, Pedro; Vale, Zita; Mourinho, Joao; Correia, R.P.

doi:10.3390/en13184774

Cited by 24 publications

(18 citation statements)

References 33 publications

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“…The previous method was presented as a point forecast with a single estimate output for each time step [41][42][43][44][45]. However, a point forecast is mainly limited to the description of the data model and the degree of uncertainty in the data.…”

Section: Probabilistic Arimax Modelmentioning

confidence: 99%

“…Typically, in the hidden units there is a role for the activation function that can be employed in order to create an output to act as input in the following layer [6,41]. Two activation functions can be broadly classified into a hyperbolic tangent (tanh) and a sigmoid [41,42]. The objective of the scalar to scalar activation function is to model non-linearity in intricate performance and restrict the output of the neuron [41].…”

Section: Ann Forecast Model Optimized By Using Golden Ratio Optimization (Grom)mentioning

confidence: 99%

“…The traditional ANN feedforward model aims to forecast the future household and Madaba city demand L (t + i) and PV system output power P (t + i); here n represents the current time step and i = 1,2, … ,24. Figure 18 illustrates and sums up the ANN model's steps and introduce the standard method for ANN [6,[40][41][42]. The steps of the ANN model in Figure 16 were pursued with the purpose of choosing appropriate parameter models, as listed below 1-Variable selection:…”

Section: Implementation Of Traditional Ann Forecast Modelsmentioning

confidence: 99%

“…, 24. Figure 18 illustrates and sums up the ANN model's steps and introduce the standard method for ANN [6,[40][41][42]. The steps of the ANN model in Figure 16 were pursued with the purpose of choosing appropriate parameter models, as listed below.…”

Section: Implementation Of Traditional Ann Forecast Modelsmentioning

confidence: 99%

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Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

et al. 2021

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More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.

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Section: Probabilistic Arimax Modelmentioning

confidence: 99%

Section: Ann Forecast Model Optimized By Using Golden Ratio Optimization (Grom)mentioning

confidence: 99%

Section: Implementation Of Traditional Ann Forecast Modelsmentioning

confidence: 99%

Section: Implementation Of Traditional Ann Forecast Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

et al. 2021

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“…The neural network model can imitate the intelligent processing of the human brain and mine different data features from a large number of multidimensional data, which completely shows that the neural network has strong self-learning and selfadaptive ability. Ramos Daniel [10] and Shahid Ali [11] et al used an artificial neural network (ANN) to improve the prediction accuracy of power consumption. With the continuous development of the neural network, Most scholars who study the electricity consumption prediction model recognize LSTM for its strong time-series learning ability and information selection ability.…”

Section: Electricity Consumption Prediction Model Based On Neural Networkmentioning

confidence: 99%

Electricity Consumption Prediction Based on Time Series Data Features Integrate with Long Short-Term Memory Model

Wang

Mou

Lin

et al. 2021

Business Intelligence and Information Technology

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The forecast of electricity consumption is of great significance to adjusting the power supply dispatching scheme and optimizing economic structure. Electricity consumption prediction is a time series prediction problem in essence. Most relevant work considers establishing electricity consumption prediction models in terms of economy, temperature, region, etc. However, few studies consider the three data characteristics of trend, seasonality, and periodicity contained in time series. Therefore, this paper proposes a Long-Short Term Memory model based on time series feature fusion. This model considers the influence of three data features of time series on time prediction results and can effectively integrate time-series features into Long Short-Term Memory Model with strong self-learning ability. Experimental results show that the proposed method has higher accuracy than the basic LSTM model and the Autoregressive Integrated Moving Average Model (ARIMA).

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The Catastrophe of Corruption in the Sustainability of Foreign aid: A Prediction of Artificial Neural Network Method in Indonesia

Paranata

Adha

Thao

et al. 2023

Fudan J. Hum. Soc. Sci.

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The rising corruption levels in Indonesia are becoming a cause for concern and raise doubts about their impact on the stability of foreign aid in the country. Therefore, this study aims to predict the long-term viability of foreign aid in Indonesia based on international perceptions of corruption and corruption cases in the country. Data were obtained from World Governance Indicators, the Indonesian Ministry of Finance, and the World Bank, and the study used a backpropagation artificial neural network (ANN) for prediction. The results from ANN are compared to linear models and vector autoregression (VAR). The finding shows that ANN outperforms the other models based on the coefficient of determination and MSE values. Furthermore, it highlights the strong relationship between corruption perception and foreign aid sustainability with an R-value of 0.991. According to the ANN estimation, gratification has a significant impact on foreign aid. In response to this finding, the study recommends the Indonesian government take action to combat corruption in maintaining the international trust and ensuring the stability of foreign aid.

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Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning

Cited by 24 publications

References 33 publications

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Electricity Consumption Prediction Based on Time Series Data Features Integrate with Long Short-Term Memory Model

The Catastrophe of Corruption in the Sustainability of Foreign aid: A Prediction of Artificial Neural Network Method in Indonesia

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