Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network

Abstract

Short-Term Load Prediction (STLP) is an important part of energy planning. STLP is based on the analysis of historical data such as outdoor temperature, heat load, heat consumer configuration, and the seasons. This research aims to forecast heat consumption during the winter heating season. By preprocessing and analyzing the data, we can determine the patterns in the data. The results of the data analysis make it possible to form learning algorithms for an artificial neural network (ANN). The biggest disadvantage of an ANN is the lack of precise guidelines for architectural design. Another disadvantage is the presence of false information in the analyzed training data. False information is the result of errors in measuring, collecting, and transferring data. Usually, trial error techniques are used to determine the number of hidden nodes. To compare prediction accuracy, several models have been proposed, including a conventional ANN and a wavelet ANN. In this research, the influence of different learning algorithms was also examined. The main differences were the training time and number of epochs. To improve the quality of the raw data and remove false information, the research uses the technology of normalizing raw data. The basis of normalization was the technology of the Z-score of the data and determination of the energy‒entropy ratio. The purpose of this research was to compare the accuracy of various data processing and neural network training algorithms suitable for use in data-driven (black box) modeling. For this research, we used a software application created in the MATLAB environment. The app uses wavelet transforms to compare different heat demand prediction methods. The use of several wavelet transforms for various wavelet functions in the research allowed us to determine the best algorithm and method for predicting heat production. The results of the research show the need to normalize the raw data using wavelet transforms. The sequence of steps involves following milestones: normalization of initial data, wavelet analysis employing quantitative criteria (energy, entropy, and energy‒entropy ratio), optimization of ANN training with information energy–entropy ratio, ANN training with different training algorithms, and evaluation of obtained outputs using statistical methods. The developed application can serve as a control tool for dispatchers during planning.

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

“…The time series of meteorological parameters for use in heating systems must concern much smaller time intervals (hours, minutes), and the methodology for forecasting such short-term data has not yet to be developed. What is more, Kováč et al [12] considered the lack of precise instructions for architectural design as the biggest problem for using neural networks. Among the research conducted so far, it is difficult to find work devoted solely to this subject.…”

Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

“…These technique outputs are much more balanced compared to that of WT, and this can easily identify weak and singular component signals. If θ(t) is the scaling function, and ψ(t) is the wavelet function, then both can be related, as [31,34]: In Equation ( 4), l im and h im denotes the low-and high-pass frequency coefficients of the signal, respectively. The full wavelet packet {δ n (t)}(n ∈ Z + ) is on the basis of δ o (t) = θ(t), which can be derived as:…”

“…On the basis of the existing literature, the third level decomposition using WPD is found to be more suitable [30][31][32][33]. However, for proper selection of decomposition level, a complete experiment has also been conducted to verify the results on the basis of their error rate.…”

“…The decomposed series has been further processed through the Group Method of Data Handling (GMDH)-based algorithm for the forecasting of load data series [30]. In 2021, the same decomposition process was carried out for temperature data in which the mother wavelet was chosen on the basis of the energy-entropy ratio; for training and testing data, a different learning algorithm-based NN was proposed [31]. On the other hand, WPD decomposes the load profile between higher and lower frequency components again into lower and higher frequency components with neural networks, and achieved almost 20% more accurate results compared to traditional WT [32].…”

The Impact of Thermo-Modernization and Predictive Regulation on Heat Energy Consumption and Emission of Pollutants into the Atmosphere - The Case of Prefabricated Buildings

“…Moreover, it is also used in forecasting thermal comfort in rooms [24][25][26]. Another issue that arises in the course of research is the importance of applying computational intelligence methods www.videleaf.com (including the neural approach) in broadly understood engineering [27][28][29].…”