Energy Demand Forecasting Using Deep Learning: Applications for the French Grid

Real, Alejandro J. del; Dorado, Fernando; Durán, Jaime

doi:10.3390/en13092242

Cited by 56 publications

(23 citation statements)

References 17 publications

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“…A sensitivity analysis was used to select the final model's hidden layers' (referred to as HL) configuration shown in Table 7. The architecture starts with a high number of neurons, then descends to lower numbers, which aligns with recent works such as [48], and the elu activation function showed slightly higher performance than the RELU activation function. Dropouts were only applied to the first two layers, which showed better performance in combination with l2 regularization.…”

Section: Deep Learning Model's Training and Optimizationsupporting

confidence: 86%

“…In this study, the deep neural networks (DNNs), which have widely been established and used in different fields, especially macroscopic short-term load forecasting (STLF) of electrical demand, are utilized and trained to perform the demand prediction [48,49]. DNNs are neural networks with more than three layers [50], used for supervised learning, in which the algorithm trains the computer to learn from given data and make future predictions or classifications.…”

Section: Day-ahead Hourly Demand Estimatiomentioning

confidence: 99%

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Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

2021

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In this paper, a comprehensive demand response model for the residential sector in the Jordanian electricity market is introduced, considering the interaction between the power generators (PGs), grid operators (GOs), and service providers (SPs). An accurate day-ahead hourly short-term load forecasting is conducted, using deep neural networks (DNNs) trained on four-year data collected from the National Electric Power Company (NEPCO) in Jordan. The customer behavior is modeled by developing a precise price elasticity matrix of demand (PEMD) based on recent research on the short-term price elasticity of Jordan’s residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys. First, the DNNs are fine-tuned with a detailed feature analysis to predict the day-ahead hourly electrical demand and achieved a mean absolute percentage error (MAPE) of 1.365% and 1.411% on the validation and test datasets receptively. Then the predictions are used as input to a detailed model of the Jordanian power grid market, where a day-ahead peak-time demand response policy for the residential sector is applied to the three distribution power companies in Jordan. Based on different PEMD analyses for the Jordanian residential sector, the results suggest a reduction potential of 5.4% in peak demand accompanied by a cost reduction of USD 154,505 per day for the Jordanian power sector.

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Section: Deep Learning Model's Training and Optimizationsupporting

confidence: 86%

Section: Day-ahead Hourly Demand Estimatiomentioning

confidence: 99%

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

2021

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“…The publication [17] has displayed a novel hybrid model ANFIS which consolidates both ANN and fuzzy frameworks for prediction future power utilization; the result has proved that this hybridizing approach has the potential of improving prediction performance since it has more significant accuracy and leads to smaller errors contrasted with other models. Likewise, the advantages of the hybrid approach were also verified by many studies [18][19][20]。.…”

Section: Literature Reviewmentioning

confidence: 59%

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Guo

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.

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“…The relation between energy price and its risk factors is not necessarily linear. Many researches in energy demand forecasting have shown the nonlinear behavior in energy demands [44,45]. Using big data mining and machine learning algorithm, one can find a nonlinear dynamic models to estimate the energy price's drivers and its worst case scenario [46,47].…”

Section: Practical Issues and Challengesmentioning

confidence: 99%

Big Data and Energy Security: Impacts on Private Companies, National Economies and Societies

Hassani

Komendantova

Kroos³

et al. 2021

Preprint

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The importance of energy security for successful functioning of private companies, national economies, and the overall society should not be underestimated. Energy is a critical infrastructure for any modern society, and its reliable functioning is essential for all economic sectors and for the well-being of everybody. Uncertainty in terms of the availability of information, reliable data to make predictions and to plan for investment as well as for other actions of stakeholders at the energy markets is one of the factors, which has the highest influence on energy security. This uncertainty can be connected with many factors such as the availability of reliable data or the actions of stakeholders themselves. For example, the recent outbreak of the COVID-19 pandemic revealed negative impacts of uncertainty on decision-making processes and markets. At the time point when the market participants started to receive real-time information about the situation, the energy markets began to ease. This is one scenario where Big Data can be used to amplify information to various stakeholders to prevent panic and to ensure market stability and security of supply. In a fast-paced digital world characterized by technological advances, the use of Big Data technology provides a unique niche point to close this gap in information disparity by levering the use of unconventional data sources to integrate technologies, stakeholders, and markets to promote energy security and market stability. The potential of Big Data technology is yet to be fully utilized. Big Data can handle large data set characterized by volume, variety, velocity, value, and complexity. The challenge for energy markets is to leverage this technology to mine available socioeconomic, political, geographic, and environmental data responsibly as well as to provide indicators that predict future global supply and demand. This information is crucial for energy security and ensuring global economic prosperity.

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Energy Demand Forecasting Using Deep Learning: Applications for the French Grid

Cited by 56 publications

References 17 publications

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Big Data and Energy Security: Impacts on Private Companies, National Economies and Societies

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