Forecasting heat load for smart district heating systems: A machine learning approach

Abstract

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 multifamily 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.

A Smart Forecasting Approach to District Energy Management

“…Idowu et al [41] proposed a forecasting approach to predict the substations' electricity demand in the district-level, which varies considerably because of households' social and financial circumstances. Therefore, predicting the household-level electricity consumption with higher granularity can improve the prediction of substation-level demand by aggregating the demand of each connected house.…”

Smart Grids Data Analysis: A Systematic Mapping Study

“…Multi Layer Perceptron (MLP) ANN (23) ( [? ], [14], [25], [42], [45], [78], [101], [110], [122], [124], [132], [156], [164], [171], [178], [187], [210], [211], [213], [228], [237], [242], [256]), Support Vector Machines (SVM) (21) ( [? ], [36], [53], [57], [65], [78], [79], [106], [115], [117], [122], [157], [159], [166], [187], [193], [203], [227], [240], [253], [256]), autoregressive integrated moving average (ARIMA) (13) ( [6], [19], [32], [42], [53], [78], …”

Energy forecasting tools and services

“…Weather dependencies can also be seen in load time series, most notably in heat load time series, which are highly dependent on ambient temperature . Load's weather dependency can be attributed to human behavior.…”

“…53 Weather dependencies can also be seen in load time series, most notably in heat load time series, which are highly dependent on ambient temperature. 54 Load's weather dependency can be attributed to human behavior. Furthermore, human behavior not only influences load depending on the weather, but also affects it differently depending on the time of day and the day of the week.…”