Short Term Power Load Forecasting using Machine Learning Models for energy management in a smart community

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

Download Full-text

Short-term Power Load Forecasting Based on Gate Recurrent Unit Network and Cloud Computing Platform

Proceedings of the 4th International Conference on Computer Science and Application Engineering ◽

10.1145/3424978.3425007 ◽

2020 ◽

Author(s):

Xiaohua Li ◽

Weijin Zhuang ◽

Hong Zhang

Keyword(s):

Cloud Computing ◽

Load Forecasting ◽

Short Term ◽

Computing Platform ◽

Power Load ◽

Power Load Forecasting ◽

Cloud Computing Platform ◽

Unit Network

Download Full-text

Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

Applied Sciences ◽

10.3390/app11094266 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4266

Author(s):

Md. Shahriare Satu ◽

Koushik Chandra Howlader ◽

Mufti Mahmud ◽

M. Shamim Kaiser ◽

Sheikh Mohammad Shariful Islam ◽

...

Keyword(s):

Machine Learning ◽

Web Application ◽

Learning Models ◽

Short Term ◽

Infected People ◽

Infected Case ◽

First Case ◽

Novel Coronavirus ◽

Short Term Prediction ◽

Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

Download Full-text

Prediction of Head Movement in 360-Degree Videos Using Attention Model

Sensors ◽

10.3390/s21113678 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3678

Author(s):

Dongwon Lee ◽

Minji Choi ◽

Joohyun Lee

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Moving Average ◽

The Other ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Attention Model ◽

Long Short Term Memory ◽

Machine Learning Models

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

Download Full-text