Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network

Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the so...

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Published in:	Energies (Basel) Vol. 15; no. 17; p. 6453
Main Authors:	Panagiotou, Dimitrios K., Dounis, Anastasios I.
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.09.2022
Subjects:	adaptive neuro-fuzzy adaptive inference system artificial neural networks Back propagation backpropagation algorithms Buildings Comparative analysis Electricity Emissions Energy consumption Energy efficiency Energy management Energy use Forecasting Forecasts and trends Genetic algorithms Greece Greenhouse gases Health facilities Hospitals long short-term memory networks Machine learning metaheuristic algorithms Neural networks Neurons Prediction theory Public buildings Public health Support vector machines Technology application Greece
ISSN:	1996-1073, 1996-1073
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Abstract	Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital’s facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is beneficial for an in-depth study on accurate predictors’ applications in Intelligent Energy Management Systems (IEMS). Three Artificial Neural Networks using a backpropagation algorithm, three Artificial Neural Networks using metaheuristic optimization algorithms for training, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Long-Short Term Memory (LSTM) network were tested using timeseries generated from a simulated healthcare facility. ANFIS and backpropagation-based trained models outperformed all other models since they both deal well with complex nonlinear problems. LSTM also performed adequately. The models trained with metaheuristic algorithms demonstrated poor performance.
AbstractList	Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital’s facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is beneficial for an in-depth study on accurate predictors’ applications in Intelligent Energy Management Systems (IEMS). Three Artificial Neural Networks using a backpropagation algorithm, three Artificial Neural Networks using metaheuristic optimization algorithms for training, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Long-Short Term Memory (LSTM) network were tested using timeseries generated from a simulated healthcare facility. ANFIS and backpropagation-based trained models outperformed all other models since they both deal well with complex nonlinear problems. LSTM also performed adequately. The models trained with metaheuristic algorithms demonstrated poor performance.
Audience	Academic
Author	Panagiotou, Dimitrios K. Dounis, Anastasios I.
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SubjectTerms	adaptive neuro-fuzzy adaptive inference system artificial neural networks Back propagation backpropagation algorithms Buildings Comparative analysis Electricity Emissions Energy consumption Energy efficiency Energy management Energy use Forecasting Forecasts and trends Genetic algorithms Greece Greenhouse gases Health facilities Hospitals long short-term memory networks Machine learning metaheuristic algorithms Neural networks Neurons Prediction theory Public buildings Public health Support vector machines Technology application
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Title	Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network
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