Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review

Dewangan, Fanidhar; Abdelaziz, Almoataz Y.; Biswal, Monalisa

doi:10.3390/en16031404

Cited by 64 publications

(23 citation statements)

References 195 publications

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“…In the proposed model architecture, a 1D-CNN is employed to extract features from the historical load dataset, where the number of filters and kernel size are crucial parameters. Various experiments were conducted with different numbers of filters (32,64,128) and different kernel sizes (3,5,7). It was observed that 128 filters with a window size of 3 generate the lowest validation loss.…”

Section: Model Architecturementioning

confidence: 99%

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Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

Baul,

Sarker,

Sikder

et al. 2024

BDCC

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Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.

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Section: Model Architecturementioning

confidence: 99%

“…Load forecasting (LF) is a critical component of power system management due to the unpredictable and inconsistent nature of load demand [2]. LF aims to predict future load demands based on current and historical data [3]. LF is commonly classified into three distinct types.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

Baul,

Sarker,

Sikder

et al. 2024

BDCC

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“…where z W and z U are the weight metrices of the update gate and z b is the bias of the update gate. Next, GRU obtains candidate hidden states through the reset gate based on the updating mechanism of RNN [34], as shown in Equation (3). It can be seen from Figure 1 that the GRU network calculates the combination degree of the current input and the previous status information by the reset gate r t .…”

Section: Bigru Neural Networkmentioning

confidence: 99%

“…where W z and U z are the weight metrices of the update gate and b z is the bias of the update gate. Next, GRU obtains candidate hidden states through the reset gate based on the updating mechanism of RNN [34], as shown in Equation (3).…”

Section: Bigru Neural Networkmentioning

confidence: 99%

A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism

Gong

2023

Energies

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Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation metrics and parametric and non-parametric testing manners are used to evaluate the proposed CS-GWO-DA-BiGRU short-term load prediction model. The experimental results show that the RMSE, MAE and SMAPE are reduced respectively by 3.86%, 1.37% and 0.30% of those of the second-best performing CSO-DA-BiGRU model, which demonstrates that the proposed model can better fit the load data and achieve better prediction results.

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“…Similar works such as Wijaya et al (2015), Hsiao (2014), andTaieb (2016) present methods to forecast and analyze consumption drivers based on smart meter data. Dewangan et al (2023) present a recent review on load forecasting models. They focus on smart meter data in smart grids.…”

Section: Introductionmentioning

confidence: 99%

A Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessing

Guzel,

Cinar,

Cenet

et al. 2023

IJEEP

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Forecasting electricity consumption is crucial for the operation planning of distribution companies and suppliers and for the success of deregulated electricity markets as a whole. Distribution companies often need consumption forecasting for meters to better plan operations and demand fulfillment. Although it is easier to forecast the aggregated demand for a region, meter based demand forecasting brings challenging issues such as non-uniform usage and uncertain customer consumption patterns. The stochastic nature of the demand for electricity, along with parameters such as temperature, humidity, and work habits, eventually causes deviations from the expected demand. In this paper, real meter data from a regional distribution company is used to cluster the customer using their non-uniform usage and automated ranking mechanism is proposed to select the best method to forecast the consumption. The proposed end-to-end methodology includes data processing, missing value detection and filling, abnormal value detection, and mass reading for meters and is applied to regional data for the period 2017-2018 and provides a powerful tool to forecasts the demand in hourly and daily horizons using only the past demand data. Besides proposing effective methodologies for data preprocessing, 10 different regression methods, 7 regressors, 5 machine learning methods that include LSTM and Ar-net models are used to forecast the meter based consumption. The hourly forecasting errors in the demand, in the Mean Absolute Percentage Error (MAPE) norm, are less than 4% for most customer groups. The meter based forecast is then aggregated to reach a final demand which is then used for operation and demand planning. The proposed framework can be considered reliable and practical in the circumstances needed to make demand and operation decisions.

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Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review

Cited by 64 publications

References 195 publications

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism

A Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessing

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