Prediction of Air-Conditioning Energy Consumption in R&amp;D Building Using Multiple Machine Learning Techniques

Liao, Jun-Mao; Chang, Ming-Jui; Chang, Luh‐Maan

doi:10.3390/en13071847

Cited by 23 publications

(6 citation statements)

References 32 publications

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“…In building energy consumption prediction, various datadriven tools have been stated to elicit good outcomes for certain granularities. For example, RF and SVM have been noted for their good outcomes in predicting long-term electricity (heating and cooling load) consumption [48,125]. However, SVM has also shown good performance among other data-driven tools for predicting long-term electricity use [93].…”

Section: Temporal Granularitiesmentioning

confidence: 99%

Data-Driven Tools for Building Energy Consumption Prediction: A Review

et al. 2023

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The development of data-driven building energy consumption prediction models has gained more attention in research due to its relevance for energy planning and conservation. However, many studies have conducted the inappropriate application of data-driven tools for energy consumption prediction in the wrong conditions. For example, employing a data-driven tool to develop a model using a small sample size, despite the recognition of the tool for producing good results in large data conditions. This study delivers a review of 63 studies with a precise focus on evaluating the performance of data-driven tools based on certain conditions; i.e., data properties, the type of energy considered, and the type of building explored. This review identifies gaps in research and proposes future directions in the field of data-driven building energy consumption prediction. Based on the studies reviewed, the outcome of the evaluation of the data-driven tools performance shows that Support Vector Machine (SVM) produced better performance than other data-driven tools in the majority of the review studies. SVM, Artificial Neural Network (ANN), and Random Forest (RF) produced better performances in more studies than statistical tools such as Linear Regression (LR) and Autoregressive Integrated Moving Average (ARIMA). However, it is deduced that none of the reviewed tools are predominantly better than the other tools in all conditions. It is clear that data-driven tools have their strengths and weaknesses, and tend to elicit distinctive results in different conditions. Hence, this study provides a proposed guideline for the selection tool based on strengths and weaknesses in different conditions.

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Section: Temporal Granularitiesmentioning

confidence: 99%

Data-Driven Tools for Building Energy Consumption Prediction: A Review

et al. 2023

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“…GRU models transfer time dependencies in the data between different time steps by a single hidden state and are trained to selectively filter out any irrelevant information while maintaining what is useful [9]. Following the same approach presented in Section 3.1, we implemented a grid search method to determine the most suitable configuration for each deep learning model [70][71][72][73][74]. The hyperparameters considered in the grid search were (i) the number of layers, and (ii) the number of nodes in each layer as summarised in Table 3.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Ribeiro¹,

Carmo²,

Endo³

et al. 2022

Preprint

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Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

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“…To determine the most suitable configuration for each selected architecture (within a specific predefined range), we use the grid search method to determine hyperparameters for learning algorithms [70][71][72][73][74]. It is used widely as it is quick to implement, trivial to parallelize, and intuitively allows an entire search space to be explored [75].…”

Section: Finding Models To Predict Energy Consumptionmentioning

confidence: 99%

Short-Term Firm-Level Energy Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models

Ribeiro¹,

Carmo²,

Rodrigues³

et al. 2020

Preprint

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To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

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Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques

Cited by 23 publications

References 32 publications

Data-Driven Tools for Building Energy Consumption Prediction: A Review

Data-Driven Tools for Building Energy Consumption Prediction: A Review

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Short-Term Firm-Level Energy Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models

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