Short term load forecasting using artificial neural network

Singh, Sarva Daman; Hussain, Shoeb; Bazaz, Mohammad Abid

doi:10.1109/iciip.2017.8313703

Cited by 122 publications

(55 citation statements)

References 3 publications

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“…The parameters are shown in Table , where L h represents the input data used to predict the loads of next day's h th hour. In order to simplify the data processing, the loads and the temperature data are normalized, and the input data should be able to both forecast the short‐term prediction accuracy and the long‐term trend, respectively . More specifically, the parameters L d , L w , T d , and T w are expected to help the model to identify the long‐term trends, and L h and T h are expected to forecast the loads and temperature of the following 24 hours.…”

Section: Model Input and The Loss Functionmentioning

confidence: 99%

“…In order to simplify the data processing, the loads and the temperature data are normalized, and the input data should be able to both forecast the short-term prediction accuracy and the long-term trend, respectively. [61][62][63] More specifically, the parameters L d , L w , T d , and T w are expected to help the model to identify the long-term trends, and L h and T h are expected to forecast the loads and temperature of the following 24 hours. The predicted loads are used to replace the value of L h and correlate the next 24 hour's loads.…”

Section: Model Input and The Loss Functionmentioning

confidence: 99%

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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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Section: Model Input and The Loss Functionmentioning

confidence: 99%

Section: Model Input and The Loss Functionmentioning

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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“…Hu et al 22 proposed an STLF model based on a generalized regression neural network (GRNN) and reported that the prediction accuracy of the model was higher than that of the backpropagation neural network (BPNN). Ertugrul 23 Since the ISO New England dataset used in previous studies [27][28][29] has extensive geographical coverage of the collected electric load, it showed uncomplicated electric energy consumption patterns. Therefore, ANN with one HL showed satisfactory prediction performance by training simple patterns adequately.…”

Section: Related Studiesmentioning

confidence: 99%

A comparative analysis of artificial neural network architectures for building energy consumption forecasting

Moon

Park

Rho

et al. 2019

International Journal of Distributed Sensor Networks

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Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists of diverse components such as smart meters, energy management systems, energy storage systems, and renewable energy resources. In particular, to make an effective energy management strategy for the energy management system, accurate load forecasting is necessary. Recently, artificial neural network–based load forecasting models with good performance have been proposed. For accurate load forecasting, it is critical to determine effective hyperparameters of neural networks, which is a complex and time-consuming task. Among these parameters, the type of activation function and the number of hidden layers are critical in the performance of neural networks. In this study, we construct diverse artificial neural network–based building electric energy consumption forecasting models using different combinations of the two hyperparameters and compare their performance. Experimental results indicate that neural networks with scaled exponential linear units and five hidden layers exhibit better performance, on average than other forecasting models.

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“…This research project highlighted the importance of minute predictions in the residential setting due to the volatile nature of household consumption and examined machine learning models that outperformed the more traditional autoregressive moving average approach. In 2017, Singh et al [10] trained an artificial neural network comprising 20 neurons in order to conduct short-term load forecasting of the NEPOOL region of ISO New England and yielded a decent Mean Absolute Percentage Error (M.A.P.E) performance while training on weekday data points. In 2018, Kuo and Huang [11] proposed the Deep Energy neural network structure, which consisted of an input layer, a feature extraction module, and a forecasting module.…”

Section: Introductionmentioning

confidence: 99%

Minutely Active Power Forecasting Models Using Neural Networks

2020

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Power forecasting is an integral part of the Demand Response design philosophy for power systems, enabling utility companies to understand the electricity consumption patterns of their customers and adjust price signals accordingly, in order to handle load demand more effectively. Since there is an increasing interest in real-time automation and more flexible Demand Response programs that monitor changes in the residential load profiles and reflect them according to changes in energy pricing schemes, high granularity time series forecasting is at the forefront of energy and artificial intelligence research, aimed at developing machine learning models that can produce accurate time series predictions. In this study we compared the baseline performance and structure of different types of neural networks on residential energy data by formulating a suitable supervised learning problem, based on real world data. After training and testing long short-term memory (LSTM) network variants, a convolutional neural network (CNN), and a multi-layer perceptron (MLP), we observed that the latter performed better on the given problem, yielding the lowest mean absolute error and achieving the fastest training time.

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Short term load forecasting using artificial neural network

Cited by 122 publications

References 3 publications

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

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

A comparative analysis of artificial neural network architectures for building energy consumption forecasting

Minutely Active Power Forecasting Models Using Neural Networks

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