Name
Title | ||
|---|---|---|
Forecasting heat load for smart district heating systems: A machine learning approach |
Abstract | ||
|---|---|---|
The rapid increase in energy demand requires effective measures to plan and optimize resources for efficient energy production within a smart grid environment. This paper presents a data driven approach to forecasting heat load for multi-family apartment buildings in a District Heating System (DHS). The forecasting model is built using six and eleven weeks of data from five building substations. The external factors and internal factors influencing the heat load in substations are parameters used as our model's input. Short-term forecast models are generated using four supervised Machine Learning (ML) techniques: Support Vector Regression (SVR), Regression Tree, Feed Forwards Neural Network (FFNN) and Multiple Linear Regression (MLR). Performance comparison among these ML methods was carried out. The effects of combining the internal and external factors influencing heat load at substations was studied. The models are evaluated with varying horizon up to 24-hours ahead. The results show that SVR has the best accuracy of 5.6% MAPE for the best-case scenario. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SmartGridComm.2014.7007705 | Smart Grid Communications |
Keywords | Field | DocType |
district heating,feedforward neural nets,load forecasting,power engineering computing,power grids,regression analysis,substations,support vector machines,trees (mathematics),FFNN,ML,MLR,SVR,data driven approach,energy demand,energy production,feedforward neural network,heat load forecasting,multiple linear regression,regression tree,smart district heating systems,smart grid environment,substations,supervised machine learning technique,support vector regression,Data driven modeling,district heating,energy efficiency,machine learning,smart cities,smart grid | Decision tree,Data-driven,Heating system,Smart grid,Efficient energy use,Support vector machine,Artificial intelligence,Engineering,Artificial neural network,Machine learning,Linear regression | Conference |
ISSN | Citations | PageRank |
2373-6836 | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel Idowu | 1 | 0 | 0.34 |
| Saguna, S. | 2 | 13 | 5.07 |
| Christer Åhlund | 3 | 215 | 27.85 |
| Olov Schelen | 4 | 3 | 2.16 |