A deep learning approach to electric energy consumption modeling

Balaji, A. Jayanth; Ram, Dhananjay; Nair, Binoy B.

doi:10.3233/jifs-169965

Cited by 17 publications

(7 citation statements)

References 9 publications

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“…Therefore, the previous methods developed to solve this problem still require refinement in terms of their actual forecasting performance. In recent years, deep learning-based approaches have been applied to various subjects relevant to a wide range of electricity demand forecasting problems (e.g., [13][14][15][16][17][18]). Various forecasting resolutions have been studied from multiple times a day (e.g., minute-by-minute [18], half-hourly [14,16], hourly [18], and every two hours [16]) to daily [15,17,18], weekly [18], monthly [13], or yearly depending on the purposes of forecasting.…”

Section: Long Short-term Memorymentioning

confidence: 99%

“…In recent years, deep learning-based approaches have been applied to various subjects relevant to a wide range of electricity demand forecasting problems (e.g., [13][14][15][16][17][18]). Various forecasting resolutions have been studied from multiple times a day (e.g., minute-by-minute [18], half-hourly [14,16], hourly [18], and every two hours [16]) to daily [15,17,18], weekly [18], monthly [13], or yearly depending on the purposes of forecasting. There have been various target units, such as a single household consumer [15,18], a business consumer (e.g., a manufacturing company in [14] and small and medium enterprise consumer in [16]), any end-user not distinguished as a household or business consumer [13], and a region or country (e.g., UT Chandigarh, India, in [17]).…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Son

Kim

2020

Sustainability

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Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

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Section: Long Short-term Memorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Son

Kim

2020

Sustainability

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“…Therefore, with the advancement of deep learning, researchers have utilized different deep learning architectures for several electricity demand forecasting problems [22][23][24][25][26][27]. Different resolutions have been studied from multiple times a day such as monthly [22], weekly [27], daily [24][25][26][27], every two hours [25], hourly [27], half-hourly [23][24][25], minute-by-minute [27]. These models studied different targets such as business consumer [23,25], household consumer [24,27], other than business or household consumer [22], and region/country such as UT Chandigarh, India, in [26].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Power Demands Using Deep Learning

et al. 2020

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The forecasting of electricity demands is important for planning for power generator sector improvement and preparing for periodical operations. The prediction of future electricity demand is a challenging task due to the complexity of the available demand patterns. In this paper, we studied the performance of the basic deep learning models for electrical power forecasting such as the facility capacity, supply capacity, and power consumption. We designed different deep learning models such as convolution neural network (CNN), recurrent neural network (RNN), and a hybrid model that combines both CNN and RNN. We applied these models to the data provided by the Korea Power Exchange. This data contains the daily recordings of facility capacity, supply capacity, and power consumption. The experimental results showed that the CNN model outperforms the other two models significantly for the three features forecasting (facility capacity, supply capacity, and power consumption).

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“…used ARIMA for the prediction of predictive energy management of HEVs. They believed with the ARIMA; consumption is reduced by around 5%-7% compared with when no predictor used (Balaji et al, 2019). and(Tokgöz and Ünal, 2018) and(Kumar et al, 2018) generated half/two-hour-ahead electric demand forecast models with CCN, GRU, ELM and LSMT and results were acceptable (Chang et al, 2018).…”

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confidence: 97%

Forecasting IRAN’s max daily demand for electricity in different weather types using deep learning approach

Abdoli¹,

Mehrara²,

Ardalani³

2019

IJET

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Electricity network management is a complicated issue that needs a forecast of consumption. This predication is generally hard and a challenging topic due to nonlinearity and permanent fluctuation of load in the network. Although linearities of time-series load may give an estimation, linearization of nonlinear data to foresee removes the nature of information which shows anomalies in electricity demand. New approaches of (ANN) help to make a forecast model with keeping information properties. In this paper, we categorized max daily load data in IRAN based on climate zone to figure the following 2 months. Long Short-Term Memory (LSTM) from deep learning approach picks and yields contrasted with different steps of epochs. The outcomes appear epochs do not necessarily improve the accuracy of prediction and best MADP for cold, tropical and temperament zone, are 1.26%, 2.5%, and 10.49%, respectively.

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A deep learning approach to electric energy consumption modeling

Cited by 17 publications

References 9 publications

A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Forecasting of Power Demands Using Deep Learning

Forecasting IRAN’s max daily demand for electricity in different weather types using deep learning approach

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