Short-term load forecasting algorithm and optimization in smart grid operations and planning

Skolthanarat, Siriya; Lewlomphaisarl, Udom; Tungpimolrut, Kanokvate

doi:10.1109/sustech.2014.7046238

Cited by 10 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In paper [ 12 ], the author used an artificial neural network to predict short-term load because of the non-linear characteristics of ANN compared to other statistical models such as ARMAX, ARMA, regression, and Kalman filtering. Using MATLAB, he did a comparative analysis of six different models of ANN based on their characteristics, performance, and simulation time.…”

Section: Literature Review and Problem Relevancementioning

confidence: 99%

“…Furthermore, by introducing a compensation factor in the load profile data, the performance outcomes have been further improved. Authors in [ 12 ] used ANN to perform load forecasts. They demonstrated that the radial basis neural network is the fastest and most accurate model of neural networks for load prediction.…”

Section: Literature Review and Problem Relevancementioning

confidence: 99%

See 1 more Smart Citation

Cluster Analysis and Model Comparison Using Smart Meter Data

Shaukat

Qadir

et al. 2021

Sensors

View full text Add to dashboard Cite

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

show abstract

Section: Literature Review and Problem Relevancementioning

confidence: 99%

Section: Literature Review and Problem Relevancementioning

confidence: 99%

Cluster Analysis and Model Comparison Using Smart Meter Data

Shaukat

Qadir

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…These models correspond to artificial intelligence algorithms that perform better with non-linear data. Feedforward Multilayer Perceptron (MLP), Support Vector Regression (SVR) , LSTM, among others, are included in this group [8]. In [9], Tang and Zhang (2011) combines ANNs with the grey optimization model for load prediction.…”

Section: Traditional Load Prediction Methods Use Statistical Models mentioning

confidence: 99%

Incremental Deep-Learning for Continuous Load Prediction in Energy Management Systems

Aragón

Puri

Graß

et al. 2019

2019 IEEE Milan PowerTech

View full text Add to dashboard Cite

In this work, we introduce load prediction as continuous input for optimization models within an optimization framework for short-term control of complex energy systems. In this context, we investigated long short-term memory (LSTM) models for load prediction, because they allow incremental training in an application with continuous real-time data and have not been used in other works for continuous load prediction to our knowledge. The test and evaluation were realized using data sets of real residential data from different locations in different time resolution-hourly and minutely. Accordingly, we tested different recurrent neural network (RNN) parameters of the model such as the number of layers, the number of hidden nodes, the inclusion of regularization, and dropout in order to find the optimal LSTM configuration for our continuous load prediction application. Besides, we analyzed the quality of the LSTM algorithm by comparing it in continuous mode with the baseline model and in batch mode with the statistical model ARIMA. Training and prediction time, as well as the error stabilization time were parameters used for the evaluation. The results showed that LSTM algorithms are highly promising for integrating continuous load prediction with incremental learning.

show abstract

“…This optimization problem requires a dayahead hourly forecast of the load, generated power, and grid price. Even though load and energy generation forecasting is a difficult task [13], multiple works have proposed various approaches to forecasting with reasonable accuracy [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Day-Ahead Energy Storage System Scheduling in Microgrid Using Genetic Algorithm and Particle Swarm Optimization

2020

View full text Add to dashboard Cite

We present a day-ahead scheduling strategy for an Energy Storage System (ESS) in a microgrid using two algorithms-Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The scheduling strategy aims to minimize the cost paid by consumers in a microgrid subject to dynamic pricing. We define an objective function for the optimization problem, present its search space, and study its structural properties. We prove that the search space has a magnification of at least 50 × (B c − B d + 1), where B c and B d are the maximum depths of charge and discharge in an hour (in percentage) of the ESS respectively. In a simulation involving load, energy generation, and grid price forecasts for three microgrids of different sizes, we obtain ESS schedules that provide average cost reductions of 11.31% (using GA) and 14.31% (using PSO) over the ESS schedule obtained using Net Power Based Algorithm.

show abstract

Short-term load forecasting algorithm and optimization in smart grid operations and planning

Cited by 10 publications

References 37 publications

Cluster Analysis and Model Comparison Using Smart Meter Data

Cluster Analysis and Model Comparison Using Smart Meter Data

Incremental Deep-Learning for Continuous Load Prediction in Energy Management Systems

Optimization of Day-Ahead Energy Storage System Scheduling in Microgrid Using Genetic Algorithm and Particle Swarm Optimization

Contact Info

Product

Resources

About