Generation and interpretation of parsimonious predictive models for load forecasting in smart heating networks

2022 ◽  
Author(s):  
Alberto Castellini ◽  
Federico Bianchi ◽  
Alessandro Farinelli
Keyword(s):  
Predictive Models ◽  
Load Forecasting ◽  
Heating Networks

scholarly journals Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods

Energy ◽  
2018 ◽  
Vol 157 ◽  
pp. 141-149 ◽  
Author(s):  
Gowri Suryanarayana ◽  
Jesus Lago ◽  
Davy Geysen ◽  
Piotr Aleksiejuk ◽  
Christian Johansson
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Thermal Load ◽  
Load Forecasting ◽  
District Heating ◽  
Selection Methods ◽  
Heating Networks

XM_HeatForecast: Heating Load Forecasting in Smart District Heating Networks

2020 ◽  
pp. 601-612
Author(s):  
Federico Bianchi ◽  
Francesco Masillo ◽  
Alberto Castellini ◽  
Alessandro Farinelli
Keyword(s):  
Load Forecasting ◽  
District Heating ◽  
Heating Load ◽  
Heating Networks

scholarly journals Operational thermal load forecasting in district heating networks using machine learning and expert advice

2018 ◽  
Vol 162 ◽  
pp. 144-153 ◽  
Author(s):  
Davy Geysen ◽  
Oscar De Somer ◽  
Christian Johansson ◽  
Jens Brage ◽  
Dirk Vanhoudt
Keyword(s):  
Machine Learning ◽  
Thermal Load ◽  
Load Forecasting ◽  
District Heating ◽  
Expert Advice ◽  
Heating Networks

Predictive model generation for load forecasting in district heating networks

2020 ◽  
pp. 1-1
Author(s):  
Alberto Castellini ◽  
Federico Bianchi ◽  
Alessandro Farinelli
Keyword(s):  
Predictive Model ◽  
Load Forecasting ◽  
District Heating ◽  
Model Generation ◽  
Heating Networks

scholarly journals A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 134911-134939 ◽  
Author(s):  
Abdullah Al Mamun ◽  
Md. Sohel ◽  
Naeem Mohammad ◽  
Md. Samiul Haque Sunny ◽  
Debopriya Roy Dipta ◽  
...  
Keyword(s):  
Predictive Models ◽  
Load Forecasting ◽  
Comprehensive Review ◽  
Forecasting Techniques

scholarly journals Short Term Load Forecasting for Electric Power Utilities: A Generalized Regression Approach Using Polynomials and Cross-Terms

2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Aneeque Ahmed Mir ◽  
Kafait Ullah ◽  
Zafar A. Khan ◽  
Furrukh Bashir ◽  
Tauseef Ur Rehman Khan ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Predictive Models ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Cross Terms ◽  
Generalized Regression

With the emergence of advanced computational technologies, the capacity to process data for developing machine learning-based predictive models has increased multifold. However, reliance on the model’s mere accuracy has swiftly shifted attention away from its interpretability. Resultantly, a need has emerged amongst forecasters and academics to have predictive models that are not only accurate but also interpretable as well. Therefore, to facilitate energy forecasters, this paper advances the knowledge of short-term load forecasting through generalized regression analysis using high degree polynomials and cross terms. To predict the irregularly changing energy demand at the consumer level, the proposed model uses a time series of an hourly load of three years of an electricity distribution company in Pakistan. Two variants of regression analysis are used: (a) generalized linear regression model (GLRM), and (b) generalized linear regression model with polynomials and cross-terms (GLRM-PCT) for comparative reasons. Experiments revealed that GLRM-PCT showed higher forecasting accuracy across a variety of performance metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and r-squared values. Moreover, the enhanced interpretability of GLRM-PCT also explained a wide range of combinations of weather variables, public holidays, as well as lagged load and climatic variables.

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