Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Runge, Jason; Zmeureanu, Radu

doi:10.3390/en12173254

Cited by 179 publications

(123 citation statements)

References 115 publications

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“…The difference between indoor and outdoor temperatures is the most important factor affecting heat demand [12][13][14][15][16][17][18][19][20]. Under the premise that the indoor temperature is set at a fixed value, the outdoor temperature is the determining factor for the heat load.…”

Section: External Temperaturementioning

confidence: 99%

“…KNN 1, 5,6,7,8,9,11,12,13,14,16,18,19 2,3,4,7,9,11,12,13,14,18,19 SVR 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19 2,3,4,11,12,13,14,17 NB 1,6,7,9,10,11,12,13,14,…”

Section: Models Electricity Heatmentioning

confidence: 99%

“…Data-driven tools, by contrast, have the power to generate models from recorded or proxy data, and these have been used in building simulations and energy performance predictions. Multiple regression and Artificial Neural Networks (ANN) represent two commonly used techniques [12].…”

Section: Introductionmentioning

confidence: 99%

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Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model

Friedrich

Harrison

2020

Energies

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There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of 'agent schedules' that provide proxy information that is based on broad classes of activity undertaken by the building's inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the 'agent schedules', which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all 'agent schedule' features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.

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Section: External Temperaturementioning

confidence: 99%

“…KNN 1, 5,6,7,8,9,11,12,13,14,16,18,19 2,3,4,7,9,11,12,13,14,18,19 SVR 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19 2,3,4,11,12,13,14,17 NB 1,6,7,9,10,11,12,13,14,…”

Section: Models Electricity Heatmentioning

confidence: 99%

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Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model

Friedrich

Harrison

2020

Energies

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“…Some of these models are developed for specific application areas such as building performance measurement and verification [14][15][16][17], building control [18][19][20] and demand-side management [21,22], whereas a significant number of studies are application agnostic. Literature demonstrates the capability of supervised ML algorithms such as artificial neural networks (ANN) [23], support vector machines (SVM) [24], decision trees [25,26], Gaussian processes [27][28][29] and nearest neighbours [30], among others, in developing reliable building load forecast models. In contrast to the physics based models, the ML based load forecast models require lesser amount of information from the buildings.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Pipeline for Demand Response Capacity Scheduling

Krishnadas¹,

Kiprakis

2020

Energies

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Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.

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“…A biological neural network consists of many interconnected biological neurons. ANNs are formed by simple units of processing, called neurons [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Francik

Kurpaska

2020

Sensors

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It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

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Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Cited by 179 publications

References 115 publications

Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model

Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model

A Machine Learning Pipeline for Demand Response Capacity Scheduling

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

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