Comparing multi-step ahead building cooling load prediction using shallow machine learning and deep learning models

Chalapathy, Raghavendra; Khoa, Nguyen Lu Dang; Sethuvenkatraman, Subbu

doi:10.1016/j.segan.2021.100543

Cited by 27 publications

(16 citation statements)

References 51 publications

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“…Based on multistep ahead predictions, which learn a simple parametric function from input time series and estimate a series of values 30 . Two methods were proposed for this investigation, which is visually represented in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

“…Based on multistep ahead predictions, which learn a simple parametric function from input time series and estimate a series of values. 30 Two methods were proposed for this investigation, which is visually represented in Figure 2. Method 1 forecasts the next 24 h for each hour of the day, allowing the method to be used to forecast at any hour of the day.…”

Section: Proposed Approachesmentioning

confidence: 99%

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Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings

Mariano-Hernández

Hernández-Callejo

Solís

et al. 2022

Energy Science & Engineering

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Buildings are one of the largest consumers of electrical energy, making it important to develop different strategies to help to reduce electricity consumption. Building energy consumption forecasting strategies are widely used to support demand management decisions, but these strategies require large data sets to achieve an accurate electric consumption forecast, so they are not commonly used for buildings with a short history of record keeping. Based on this, the objective of this study is to determine, through continuous hourly electricity consumption forecasting strategies, the amount of data needed to achieve an accurate forecast. The proposed forecasting strategies were evaluated with Random Forest, eXtreme Gradient Boost, Convolutional Neural Network, and Temporal Convolutional Network algorithms using 4 years of electricity consumption data from two buildings located on the campus of the University of Valladolid.For performance evaluation, two scenarios were proposed for each of the proposed forecasting strategies. The results showed that for forecasting horizons of 1 week, it was possible to obtain a mean absolute percentage error (MAPE) below 7% for Building 1 and a MAPE below 10% for Building

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Section: Methodsmentioning

confidence: 99%

Section: Proposed Approachesmentioning

confidence: 99%

Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings

Mariano-Hernández

Hernández-Callejo

Solís

et al. 2022

Energy Science & Engineering

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show abstract

“…By comparing relevance scores obtained from the TensorFlow to timesteps and covariates, we evaluated their contributions to PM2.5 prediction [27], [28], [29]. The RNN model was retrained and tested using only the timesteps and variables with high relevance scores to see if the primary input time-steps and variables provided by the TensorFlow had significantly contributed to the prediction [30], [31], [32]. The structure of this document is as follows.…”

Section: Levels In Recognition Of the Severely Harmful Impactsmentioning

confidence: 99%

The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5

Rahardja¹,

Aini²,

Sunarya³

et al. 2022

att

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Artificial intelligence techniques to forecasts based on the Community Multiscale Air Quality (PM2.5) operational model can be known using TensorFlow. TensorFlow was used in this study to assess the scores of the Recurrent Neural Networks (RNN) input variables on the 6-hour forecast for July-October 2022. The relevance scores for the one- and two-day forecasts are represented by the sum of the relevance scores across the target prediction timeframe 2–5 and 4–7 previous time steps. The initial selection of input variables was based on their correlation coefficient with the measured PM2.5 concentration. Still, the order of contribution of the input variables measured by TensorFlow was different from the order of their correlation coefficients, which indicated an inconsistency between the linear and nonlinear variables of the method. It was found that the retraining of the RNN model using a subset of variables with a high relevance score resulted in a predictive ability similar to the initial set of input variables.

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“…While short-term load predictions on an hourly basis or at a more reduced time granularity help to dynamically control the chilled water flow rates of the chiller, long-term load predictions, such as dayahead forecasts, help to plan the next day's energy demands. Using day-ahead cooling load predictions, additional demands can be satisfied easily, resources can be managed efficiently, or a proper demand-response system can be activated [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Day-Ahead Cooling Load Prediction in Tropical Commercial Buildings Using Advanced Deep Learning Models: A Case Study in Singapore

Kondath,

Myat,

Soh

et al. 2024

Buildings

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Commercial buildings in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This paper aims to address this gap by investigating the application of innovative deep-learning models in day-ahead hourly cooling load prediction for commercial buildings in tropical climates. A range of multi-output deep learning techniques, including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs), are employed to enhance prediction accuracy. Furthermore, these individual deep learning techniques are synergistically integrated to create hybrid models, such as CNN-LSTM and Sequence-to-Sequence models. Experiments are conducted to choose the time horizons from the past that can serve as input to the models. In addition, the influence of various categories of input parameters on prediction performance has been assessed. Historical cooling load, calendar features, and outdoor weather parameters are found in decreasing order of influence on prediction accuracy. This research focuses on buildings located in Singapore and presents a comprehensive case study to validate the proposed models and methodologies. The sequence-to-sequence model provided better performance than all the other models. It offered a CV-RMSE of 7.4%, 10%, and 6% for SIT@Dover, SIT@NYP, and the simulated datasets, which were 2.3%, 3%, and 1% less, respectively, than the base Deep Neural Network model.

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Comparing multi-step ahead building cooling load prediction using shallow machine learning and deep learning models

Cited by 27 publications

References 51 publications

Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings

Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings

The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5

Enhancing Day-Ahead Cooling Load Prediction in Tropical Commercial Buildings Using Advanced Deep Learning Models: A Case Study in Singapore

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