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

Dimitrios K. Panagiotou and Anastasios I. Dounis ()
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Dimitrios K. Panagiotou: Department of Biomedical Engineering, University of West Attica, 12243 Athens, Greece
Anastasios I. Dounis: Department of Biomedical Engineering, University of West Attica, 12243 Athens, Greece

Energies, 2022, vol. 15, issue 17, 1-25

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.

Keywords: artificial neural networks; adaptive neuro-fuzzy adaptive inference system; long short-term memory networks; backpropagation algorithms; metaheuristic algorithms; machine learning; load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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