Commercial Building Load Forecasts with Artificial Neural Network

Jing, Zejia; Cai, Mengmeng; Pipattanasomporn, Manisa; Rahman, Saifur; Kothandaraman, Raghavan; Malekpour, Ahmadreza; Paaso, Esa Aleksi; Bahramirad, Shay

doi:10.1109/isgt.2019.8791654

Cited by 11 publications

(2 citation statements)

References 10 publications

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“…Among them, ANN has strong adaptability, self-organization and self-learning ability [50,51], and has obvious advantages in dealing with load data with nonlinear characteristics [52][53][54][55]. It has been proved that ANN has good prediction performance in the cooling and heating load prediction of institutional buildings [56][57][58], district buildings [49,55] and other scenarios. Therefore, the ANN is employed in constructing a short-term forecasting model of air conditioning load based on the IDT.…”

Section: Load Forecasting Modelmentioning

confidence: 99%

A model predictive control regulation model for radiant air conditioning system based on delay time

Zhao

Shi

et al. 2022

Journal of Building Engineering

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Section: Load Forecasting Modelmentioning

confidence: 99%

A model predictive control regulation model for radiant air conditioning system based on delay time

Zhao

Shi

et al. 2022

Journal of Building Engineering

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“…Predictor methods for heating load based on artificial neural networks (ANN) have been evaluated in [18] for office buildings where the impact of data size and dimensionality in ANN was inspected. In order for heating, ventilation, and air-conditioning (HVAC) system optimization in [19], electricity load forecasting based on ANN has been studied. Among three utilized algorithms such as Levenberg-Marquardt, Scaled Conjugate gradient back-propagation, and Bayesian Regularization (BR), the BR-based ANN showed the best performance.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

et al. 2020

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Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

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Energy load forecasting: one-step ahead hybrid model utilizing ensembling

Tsalikidis,

Mystakidis,

Tjortjis

et al. 2023

Computing

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In the light of the adverse effects of climate change, data analysis and Machine Learning (ML) techniques can provide accurate forecasts, which enable efficient scheduling and operation of energy usage. Especially in the built environment, Energy Load Forecasting (ELF) enables Distribution System Operators or Aggregators to accurately predict the energy demand and generation trade-offs. This paper focuses on developing and comparing predictive algorithms based on historical data from a near Zero Energy Building. This involves energy load, as well as temperature data, which are used to develop and evaluate various base ML algorithms and methodologies, including Artificial Neural Networks and Decision-trees, as well as their combination. Each algorithm is fine-tuned and tested, accounting for the unique data characteristics, such as the presence of photovoltaics, in order to produce a robust approach for One-Step-Ahead ELF. To this end, a novel hybrid model utilizing ensemble methods was developed. It combines multiple base ML algorithms the outputs of which are utilized to train a meta-model voting regressor. This hybrid model acts as a normalizer for any new data input. An experimental comparison of the model against unseen data and other ensemble approaches, showed promising forecasting results (mean absolute percentage error = 5.39%), particularly compared to the base algorithms.

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Commercial Building Load Forecasts with Artificial Neural Network

Cited by 11 publications

References 10 publications

A model predictive control regulation model for radiant air conditioning system based on delay time

A model predictive control regulation model for radiant air conditioning system based on delay time

Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

Energy load forecasting: one-step ahead hybrid model utilizing ensembling

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