A Thermal Control Methodology Based on a Machine Learning Forecasting Model for Indoor Heating

2021 ◽  
pp. 111692
Author(s):  
Makram Abdellatif ◽  
Julien Chamoin ◽  
Jean-Marie Nianga ◽  
Didier Defer
Keyword(s):  
Machine Learning ◽  
Thermal Control ◽  
Forecasting Model ◽  
Control Methodology

scholarly journals Electricity demand forecasting in industrial and residential facilities using ensemble machine learning

2020 ◽  
Author(s):  
Rodrigo Porteiro ◽  
Luis Hernández-Callejo ◽  
Sergio Nesmachnow
Keyword(s):  
Machine Learning ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Residential Facilities ◽  
Forecasting Model ◽  
Model Based ◽  
Electricity Demand Forecasting ◽  
Use Of Resources ◽  
Ensemble Machine Learning ◽  
Ensemble Strategy

This article presents electricity demand forecasting models for industrial and residential facilities, developed using ensemble machine learning strategies. Short term electricity demand forecasting is beneficial for both consumers and suppliers, as it allows improving energy efficiency policies and the rational use of resources. Computational intelligence models are developed for day-ahead electricity demand forecasting. An ensemble strategy is applied to build the day-ahead forecasting model based on several one-hour models. Three steps of data preprocessing are carried out, including treating missing values, removing outliers, and standardization. Feature extraction is performed to reduce overfitting, reducing the training time and improving the accuracy. The best model is optimized using grid search strategies on hyperparameter space. Then, an ensemble of 24 instances is generated to build the complete day-ahead forecasting model. Considering the computational complexity of the applied techniques, they are developed and evaluated on the National Supercomputing Center (Cluster-UY), Uruguay. Three different real data sets are used for evaluation: an industrial park in Burgos (Spain), the total electricity demand for Uruguay, and demand from a distribution substation in Montevideo (Uruguay). Standard performance metrics are applied to evaluate the proposed models. The main results indicate that the best day ahead model based on ExtraTreesRegressor has a mean absolute percentage error of 2:55% on industrial data, 5:17% on total consumption data and 9:09% on substation data. 

scholarly journals Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia

2020 ◽  
Author(s):  
Wanie M. Ridwan ◽  
Michelle Sapitang ◽  
Awatif Aziz ◽  
Khairul Faizal Kushiar ◽  
Ali Najah Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Forecasting Model ◽  
Rainfall Forecasting ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model

Smart Water ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Mo’tamad Bata ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Hybrid Models ◽  
Supervised Machine Learning ◽  
Forecasting Model ◽  
Short Term ◽  
Unsupervised Machine Learning ◽  
Water Demand Forecasting ◽  
Clustering Model

Abstract Regression Tree (RT) forecasting models are widely used in short-term demand forecasting. Likewise, Self-Organizing Maps (SOM) models are known for their ability to cluster and organize unlabeled big data. Herein, a combination of these two Machine Learning (ML) techniques is proposed and compared to a standalone RT and a Seasonal Autoregressive Integrated Moving Average (SARIMA) models, in forecasting the short-term water demand of a municipality. The inclusion of the Unsupervised Machine Learning clustering model has resulted in a significant improvement in the performance of the Supervised Machine Learning forecasting model. The results show that using the output of the SOM clustering model as an input for the RT forecasting model can, on average, double the accuracy of water demand forecasting. The Mean Absolute Percentage Error (MAPE) and the Normalized Root Mean Squared Error (NRMSE) were calculated for the proposed models forecasting 1 h, 8 h, 24 h, and 7 days ahead. The results show that the hybrid models outperformed the standalone RT model, and the broadly used SARIMA model. On average, hybrid models achieved double accuracy in all 4 forecast periodicities. The increase in forecasting accuracy afforded by this hybridized modeling approach is encouraging. In our application, it shows promises for more efficient energy and water management at the water utilities.

Demand forecasting at retail stage for selected vegetables: a performance analysis

2019 ◽  
Vol 14 (4) ◽  
pp. 1042-1063 ◽  
Author(s):  
Rahul Priyadarshi ◽  
Akash Panigrahi ◽  
Srikanta Routroy ◽  
Girish Kant Garg
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Forecast Error ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Forecasting Model ◽  
Content Type ◽  
Forecasting Models

Purpose The purpose of this study is to select the appropriate forecasting model at the retail stage for selected vegetables on the basis of performance analysis. Design/methodology/approach Various forecasting models such as the Box–Jenkins-based auto-regressive integrated moving average model and machine learning-based algorithms such as long short-term memory (LSTM) networks, support vector regression (SVR), random forest regression, gradient boosting regression (GBR) and extreme GBR (XGBoost/XGBR) were proposed and applied (i.e. modeling, training, testing and predicting) at the retail stage for selected vegetables to forecast demand. The performance analysis (i.e. forecasting error analysis) was carried out to select the appropriate forecasting model at the retail stage for selected vegetables. Findings From the obtained results for a case environment, it was observed that the machine learning algorithms, namely LSTM and SVR, produced the better results in comparison with other different demand forecasting models. Research limitations/implications The results obtained from the case environment cannot be generalized. However, it may be used for forecasting of different agriculture produces at the retail stage, capturing their demand environment. Practical implications The implementation of LSTM and SVR for the case situation at the retail stage will reduce the forecast error, daily retail inventory and fresh produce wastage and will increase the daily revenue. Originality/value The demand forecasting model selection for agriculture produce at the retail stage on the basis of performance analysis is a unique study where both traditional and non-traditional models were analyzed and compared.

Sign in / Sign up

Export Citation Format

Share Document