Deep neural networks for energy load forecasting

Amarasinghe, Kasun; Marino, Daniel L.; Manic, Milos

doi:10.1109/isie.2017.8001465

Cited by 273 publications

(139 citation statements)

References 24 publications

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“…Recently, some methods have been proposed in literature such as neural networks or wavelet analysis method, which will be used for comparison with the proposed method. The neural networks method is an advanced method that uses a pattern recognition model to predict load . This method is based on the concept of the neural networks of “learning from the past” in order to predict the future.…”

Section: Proposed Methods For Energy Forecastingmentioning

confidence: 99%

“…The neural networks method is an advanced method that uses a pattern recognition model to predict load. 3,15,27,28 This method is based on the concept of the neural networks of "learning from the past" in order to predict the future. One should alternatively educate the neural network models to get more accurate results.…”

Section: Logistic Methodsmentioning

confidence: 99%

“…In literature, there are different methods for forecasting the future energy . In Li et al a method has been proposed for short‐term load forecasting based on wavelet transform and partial least squares regression.…”

Section: Introductionmentioning

confidence: 99%

“…In literature, there are different methods for forecasting the future energy. [1][2][3][4][5][6][7][8][9] In Li et al 4 a method has been proposed for short-term load forecasting based on wavelet transform and partial least squares regression. In another research, hourly loads and peak load for the next selected time span have been predicted using hourly loads of successive years.…”

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confidence: 99%

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A new combined method for future energy forecasting in electrical networks

Baesmat

Shiri

2018

Int Trans Electr Energ Syst

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Summary Growing the industry and population in a region leads to the growth of required amount of electrical energy. Electrical companies should provide high quality energy according to the demand of customers. Load is an effective parameter of any system, and the network programmers should consider the annual load growth of the system and predict the required energy for each region by investigating previously recorded data of consumed electrical energy and stochastic analysis. In this paper, a new combined method for long‐term energy forecasting is proposed. This method, which combines the land‐consumption method and curve fitting based on generalization method, in addition to having simple calculations, takes into account the saturation. Moreover, loads from detailed formal program provided by the relevant institutes have been used, which results in better coordination between all organizations in charge of energy predictions and development of the countries. A suitable filtering method is also employed for input data to improve the method accuracy. To show the effectiveness of the proposed method, the results of different methods have been compared with those of the proposed method as well as real data. Then, real data of former 11 years of consumed energy gathered from Shiraz Electrical Distribution Company subscribers are employed and the energy for future 11 years is forecasted.

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Section: Proposed Methods For Energy Forecastingmentioning

confidence: 99%

Section: Logistic Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A new combined method for future energy forecasting in electrical networks

Baesmat

Shiri

2018

Int Trans Electr Energ Syst

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“…In this paper, we presented an end-to-end neural network model to predict the loads for the following 24 hours. Since the DL can accurately predict the energy load, [35][36][37][38][39][40][41] three deep learning algorithms, including the multi-layer perceptron (MLP), 42 convolutional neural network (CNN), 43 and long-short term memory (LSTM), 44 are chosen for analysis.…”

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confidence: 99%

An effective deep learning neural network model for short‐term load forecasting

Wang

et al. 2020

Concurrency and Computation

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Summary Energy load forecasting plays an important role in the smart grid, which can affect the promoting energy production and consumption decision‐making processes. In this paper, the state‐of‐the‐art deep learning (DL) neural models are used in the short‐term load forecasting, including the multilayer perceptron (MLP), the convolutional neural network (CNN), and the long short‐term memory (LSTM). A novel loss function is proposed for the load forecasting, and two commonly used benchmarks are used to verify the validity of the proposed function. The simulation results show that the mean absolute percentage error (MAPE) of the proposed loss function is 19.63% lower than cross‐entropy and 2.34% lower than mean absolute error (MAE). We compared the mentioned neural networks in different aspects, and the results show that in energy load forecasting, CNN has superior performance than MLP and LSTM in terms of high accuracy and robustness to weather changes.

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Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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Deep neural networks for energy load forecasting

Cited by 273 publications

References 24 publications

A new combined method for future energy forecasting in electrical networks

A new combined method for future energy forecasting in electrical networks

An effective deep learning neural network model for short‐term load forecasting

Deep learning and reinforcement learning approach on microgrid

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