# Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology and Approach

#### 2.1. Overview

#### 2.2. Assumptions

#### 2.3. Building Energy Simulation Models

#### 2.4. Dataset Generation

- Training set 1:
- ○
- Three year simulation for three locations (Helsinki, Jyväskylä, Sodankylä), 100 buildings per type per location.
- ○
- Weather data includes years 2016–2018.
- ○
- Contains a total of 900 buildings.
- ○
- Training set is divided so that 90% is used for training and 10% for validation.

- Training set 2:
- ○
- One month simulation for Helsinki only, 300 buildings per building type.
- ○
- Simulated months are sampled randomly from 2015 weather data.
- ○
- Contains a total of 10,800 buildings.
- ○
- Training set is divided so that 90% is used for training and 10% for validation.

- Training set 3:
- ○
- Training set 1 and 2 combined (added together as such).

- Test set:
- ○
- One year simulation for each of the three locations, 20 building per building type.
- ○
- Year 2019 measurements are used as the weather data.
- ○
- Contains a total of 180 buildings.

#### 2.5. Feed Forward Neural Network

#### 2.6. Metrics

^{2}) are calculated for each building in the test set separately. In the results, the average of these values are presented based on the building type and location. Formulas used for calculating the metrics are presented in Equations (1)–(3).

_{i}is the observation, $\overline{y}$ is its mean, and ŷ

_{i}is predicted value, y

_{max}and y

_{min}are the maximum and minimum values of y, respecctively.

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**One year examples of measured (red) and forecast (blue) heating load for different building types (apartment, office, single family home).

**Figure 3.**Six week examples of measured (red) and forecast (blue) heating load for different building types (apartment, office, single family home).

Attribute | Value |
---|---|

Number of layers | 5 |

Number of neurons in total | 5121 |

Activation function for hidden layers | RELU |

Optimization method | Adam [22] |

Input scaling | MinMaxScaler |

Training Set | Apartment Buildings | Offices | Single Family Houses |
---|---|---|---|

(1) training set 1 | 0.064 | 0.031 | 0.059 |

(2) training set 2 | 0.063 | 0.035 | 0.060 |

(3) training set 3 | 0.058 | 0.026 | 0.052 |

Training Set | Apartment Buildings | Offices | Single Family Houses |
---|---|---|---|

(1) training set 1 | 0.89 | 0.95 | 0.92 |

(2) training set 2 | 0.91 | 0.93 | 0.93 |

(3) training set 3 | 0.92 | 0.96 | 0.94 |

Training Set | Helsinki | Jyväskylä | Sodankylä |
---|---|---|---|

(1) training set 1 | 0.053 | 0.048 | 0.054 |

(2) training set 2 | 0.051 | 0.050 * | 0.057 * |

(3) training set 3 | 0.048 | 0.042 | 0.047 |

Training Set | Helsinki | Jyväskylä | Sodankylä |

(1) training set 1 | 0.91 | 0.93 | 0.92 |

(2) training set 2 | 0.92 | 0.93 * | 0.93 * |

(3) training set 3 | 0.94 | 0.95 | 0.95 |

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**MDPI and ACS Style**

Kannari, L.; Kiljander, J.; Piira, K.; Piippo, J.; Koponen, P.
Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator. *Forecasting* **2021**, *3*, 290-302.
https://doi.org/10.3390/forecast3020019

**AMA Style**

Kannari L, Kiljander J, Piira K, Piippo J, Koponen P.
Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator. *Forecasting*. 2021; 3(2):290-302.
https://doi.org/10.3390/forecast3020019

**Chicago/Turabian Style**

Kannari, Lotta, Jussi Kiljander, Kalevi Piira, Jouko Piippo, and Pekka Koponen.
2021. "Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator" *Forecasting* 3, no. 2: 290-302.
https://doi.org/10.3390/forecast3020019