Improved Deep Learning Model Based on Self-Paced Learning for Multiscale Short-Term Electricity Load Forecasting

Electricity loads are basic and important information for power generation facilities and traders, especially in terms of production plans, daily operations, unit commitments, and economic dispatches. Short-term load forecasting (STLF), which predicts power loads for a few days, plays a vital role in the reliable, safe, and efficient operation of a power system. Currently, two main challenges are faced by existing STLF prediction models. The first involves how to fuse multiscale electricity load data to obtain a high-performance model and remove data noise after integration. The second involves how to improve the local optimal solution despite the sample quality problem. To address the above issues, this paper proposes a multiscale electricity load data fusion- and STLF-based short time series prediction model built on a sparse deep autoencoder and self-paced learning (SPL). A sparse deep autoencoder was used to solve the multiscale data fusion problem with data noise. Furthermore, SPL was utilized to solve the local optimal solution problem. The experimental results showed that our model was better than the existing STLF prediction models by more than 15.89% in terms of the mean squared error (MSE) indicator.

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An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Sensors ◽

10.3390/s21051639 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1639

Author(s):

Seungmin Jung ◽

Jihoon Moon ◽

Sungwoo Park ◽

Eenjun Hwang

Keyword(s):

Power Consumption ◽

Prediction Models ◽

Short Term Memory ◽

Load Forecasting ◽

Input Sequence ◽

Short Term ◽

Performance Improvements ◽

Short Term Load Forecasting ◽

Significant Performance ◽

Input Variables

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

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A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

Applied Sciences ◽

10.3390/app11062742 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2742

Author(s):

Fatih Ünal ◽

Abdulaziz Almalaq ◽

Sami Ekici

Keyword(s):

Deep Learning ◽

Energy Consumption ◽

Prediction Models ◽

Short Term Memory ◽

Critical Role ◽

Load Forecasting ◽

Unified Framework ◽

Short Term ◽

Planning And Scheduling ◽

Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

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Analysis of temperature-sensitive on short-term electricity load forecasting

2020 IEEE-HYDCON ◽

10.1109/hydcon48903.2020.9242910 ◽

2020 ◽

Author(s):

Nguyen Xuan Tung ◽

Nguyen Quang Dat ◽

Tran Ngoc Thang ◽

Vijender Kumar Solanki ◽

Nguyen Thi Ngoc Anh

Keyword(s):

Load Forecasting ◽

Temperature Sensitive ◽

Short Term ◽

Electricity Load ◽

Electricity Load Forecasting

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Deep-learning-based short-term electricity load forecasting: A real case application

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2021.104645 ◽

2022 ◽

Vol 109 ◽

pp. 104645

Author(s):

Ibrahim Yazici ◽

Omer Faruk Beyca ◽

Dursun Delen

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

Real Case ◽

Short Term ◽

Electricity Load ◽

Case Application ◽

Electricity Load Forecasting

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Day-ahead load forecasting using improved grey Verhulst model

Journal of Engineering Design and Technology ◽

10.1108/jedt-12-2019-0337 ◽

2020 ◽

Vol 18 (5) ◽

pp. 1335-1348

Author(s):

Ariel Mutegi Mbae ◽

Nnamdi I. Nwulu

Keyword(s):

Power Systems ◽

Unit Commitment ◽

Load Forecasting ◽

Forecasting Model ◽

Short Term ◽

Content Type ◽

Dispatch System ◽

System Maintenance ◽

Electricity Load ◽

Generator Unit

Purpose In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model. Design/methodology/approach To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019. Findings The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent. Practical implications In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters. Social implications In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages. Originality/value This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

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