Ensemble of radial basis neural networks with k-means clustering for heating energy consumption prediction

U radu je predložen i prikazan ansambl neuronskih mreža za predviđanje potrošnje toplote univerzitetskog kampusa. Za obučavanje i testiranje modela korišćeni su eksperimentalni podaci. Razmatrano je poboljšanje tačnosti predviđanja primenom k-means metode klasterizacije za generisanje obučavajućih podskupova neuronskih mreža zasnovanih na radijalnim bazisnim funkcijama. Korišćen je različit broj klastera, od 2-5. Izlazi članova ansambla su kombinovani primenom aritmetičkog, težinskog i osrednjavanja metodom medijane. Pokazano je da ansambli neuronskih mreža ostvaruju bolje rezultate predviđanja nego svaka pojedinačna mreža članica ansambla. PR Data used for this paper were gathered during study visit to NTNU, as a part of the collaborative project: Sustainable energy and environment in Western Balkans.For the prediction of heating energy consumption of university campus, neural network ensemble is proposed. Actual measured data are used for training and testing the models. Improvement of the prediction accuracy using k-means clustering for creating subsets used to train individual radial basis function neural networks is examined. Number of clusters is varying from 2 to 5. The outputs of ensemble members are aggregated using simple, weighted and median based averaging. It is shown that ensembles achieve better prediction results than the individual network

Multistage ensemble of feedforward neural networks for prediction of heating energy consumption

Feedforward neural network models are created for prediction of heating energy consumption of a university campus. Actual measured data are used for training and testing the models. Multistage neural network ensemble is proposed for the possible improvement of prediction accuracy. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as a member of the ensemble. Three different averaging methods (simple, weighted, and median) for obtaining ensemble output are applied. Besides this conventional approach, single radial basis neural network in the second level is used to aggregate the selected ensemble members. It is shown that heating energy consumption can be predicted with better accuracy by using ensemble of neural networks than using the best trained single neural network, while the best results are achieved with multistage ensemble

Multi-Objective Optimisation for Tuning Building Heating and Cooling Loads Forecasting Models

Machine learning (ML) has been recognised as a powerful method for modelling building energy consumption. The capability of ML to provide a fast and accurate prediction of energy loads makes it an ideal tool for decision-making tasks related to sustainable design and retrofit planning. However, the accuracy of these ML models is much dependant on the selection of the right hyper-parameters for specific building dataset. This paper proposes a method for optimising ML model for forecasting both heating and cooling loads. The technique employs multi-objective optimisation with evolutionary algorithms to search the space of possible parameters. The proposed approach not only tune one model to precisely predict building energy loads but also accelerates the process of model optimisation. The study utilises a simulated building energy data generated in EnergyPlus to demonstrate the efficiency of the proposed method, and compares the outcomes with the regular ML tuning procedure (i.e. grid search). The optimised model provides a reliable tool for building designers and engineers to explore a large space of the available building materials and technologies

Forecasting Natural Gas: A Literature Survey

This work presents a state-of-the-art survey of published papers that forecast natural gas production, consumption or demand, prices and income elasticity, market volatility and hike in prices. New models and techniques that have recently been applied in the field of natural gas forecasting have discussed with highlights on various methodologies, their specifics, data type, data size, data source, results and conclusions. Moreover, we undertook the difficult task of classifying existing models that have been applied in this field by giving their performance for instance. Our objective is to provide a synthesis of published papers in the field of natural gas forecasting, insights on modeling issues to achieve usable results, and the future research directions. This work will help future researchers in the area of forecasting no matter the methodological approach and nature of energy source used. Keywords: Forecasting natural gas; Existing forecasting models; Models categorization. JEL Classifications: C53, Q4, Q4

Various multistage ensembles for prediction of heating energy consumption

Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble

Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: a review

The strict CO2 emission targets set to tackle the global climate change associated with greenhouse gas emission exerts so much pressure on our cities which contribute up to 75% of the global carbon dioxide emission level, with buildings being the largest contributor (UNEP, 2015). Premised on this fact, urban planners are required to implement proactive energy planning strategies not only to meet these targets but also ensure that future cities development is performed in a way that promotes energy-efficiency. This article gives an overview of the state-of-art of energy planning and forecasting approaches for aiding physical improvement strategies in the building sector. Unlike previous reviews, which have only addressed the strengths as well as weaknesses of some of the approaches while referring to some relevant examples from the literature, this article focuses on critically analysing more approaches namely; 2D GIS and 3DGIS (CityGML) based energy prediction approaches, based on their frequent intervention scale, applicability in the building life cycle, and conventional prediction process. This will be followed by unravelling the gaps and issues pertaining to the reviewed approaches. Finally, based on the identified problems, future research prospects are recommended

Prediction of hourly heating energy use for HVAC using feedforward neural networks

In this paper, feedforward neural network, one of the most widely used artificial intelligence methods, was proposed for the prediction of hourly heating energy use of one university campus. Two different approaches were presented: network that provides one output (heating energy use for selected hour) and network with 24 outputs (daily profile of heating energy use). The proposed models were trained and tested using real measured hourly energy use and meteorological data. It has been shown that both models can be used for the prediction with satisfying accuracy. This kind of prediction can be used for calculating accurate energy bills, which is very useful considering that the significant part of the campus is being leased. Estimating energy use for different weather conditions can help in energy planning

Data Driven Model Improved by Multi-Objective Optimisation for Prediction of Building Energy Loads

Machine learning (ML) has been recognised as a powerful method for modelling building energy consumption. The capability of ML to provide a fast and accurate prediction of energy loads makes it an ideal tool for decision-making tasks related to sustainable design and retrofit planning. However, the accuracy of these ML models is dependent on the selection of the right hyper-parameters for a specific building dataset. This paper proposes a method for optimising ML models for forecasting both heating and cooling loads. The technique employs multi-objective optimisation with evolutionary algorithms to search the space of possible parameters. The proposed approach not only tunes single model to precisely predict building energy loads but also accelerates the process of model optimisation. The study utilises simulated building energy data generated in EnergyPlus to validate the proposed method, and compares the outcomes with the regular ML tuning procedure (i.e. grid search). The optimised model provides a reliable tool for building designers and engineers to explore a large space of the available building materials and technologies

Prediction of hourly heating energy use for HVAC using feedforward neural networks

In this paper, feedforward neural network, one of the most widely used artificial intelligence methods, was proposed for the prediction of hourly heating energy use of one university campus. Two different approaches were presented: network that provides one output (heating energy use for selected hour) and network with 24 outputs (daily profile of heating energy use). The proposed models were trained and tested using real measured hourly energy use and meteorological data. It has been shown that both models can be used for the prediction with satisfying accuracy. This kind of prediction can be used for calculating accurate energy bills, which is very useful considering that the significant part of the campus is being leased. Estimating energy use for different weather conditions can help in energy planning

Predviđanje potrošnje toplote u univerzitetskom kampusu korišćenjem neuronske mreže

In this paper, the main objective is to predict heating energy consumption using a simple artificial neural network. For training and testing the network daily consumption for NTNU University campus Gløshaugen and mean outside temperatures were used. Training of the network was performed by using Levenberg–Marquardt (LM) feedforward backpropagation algorithm. Different indices of the prediction accuracy were calculated for training and testing. Simplified model showed that it can predict heating consumption with adequate accuracy. Creating a model of energy use helps in future building planning; it can provide useful information about most probable energy consumption for similar buildings, or predict energy use in different conditions.Tema ovog rada je predviđanje potrošnje toplote korišćenjem pojednostavljene neuronske mreže. Za obučavanje i testiranje mreže korišćene su dnevne potrošnje toplote u univerzitetskom kampusu NTNU Gløshaugen, kao i srednje dnevne spoljne temperature. Za obučavanje mreže korišćen je Levenberg–Marquardt (LM) feedforward backpropagation algoritam (algoritam sa povratnim prostiranjem greške). Različiti pokazatelji kvaliteta predviđenja su izračunati i prikazani za obučavanje i testiranje mreže. Pokazuje se da pojednostavljen model može da predvidi potrošnju toplote sa zadovoljavajuće visokom tačnošću. Kreiranje ovakvih modela je korisno s aspekta energetskog planiranja izgradnje; pruža informacije o verovatnoj potrošnji toplote za slične zgrade, ili predviđa potrošnju pri različitim vremenskim uslovima