The aim of the paper is to facilitate energy suppliers to make decisions for the provision of energy to different residential buildings according to their demand, which will enable the energy suppliers to manage and optimize the energy... more
The aim of the paper is to facilitate energy suppliers to make decisions for the provision of energy to different residential buildings according to their demand, which will enable the energy suppliers to manage and optimize the energy consumption in an efficient manner. In this paper, we have used Multi-layer perceptron and Random Forest to classify residential buildings according to their energy consumption. The hourly consumed historical data, of two types of buildings, have been predicted: high power and low power consumption buildings. The prediction consists of three stages: data retrieval, feature extraction, and prediction. In the data retrieval stage, the hourly consumed data based on the daily basis is retrieved from the database. In the feature extraction stage, statistical features; mean, standard deviation, skewness and kurtosis are computed from the retrieved data. In the prediction stage, Multi-Layer Perceptron and Random Forest have been used for the prediction of high power and low power consumption buildings. The hourly consumed historical data of 400 residential buildings have been used for experimentation. The data was divided into 70% (280 buildings) training and 30% (120 buildings) testing. The Multi-Layer Perceptron achieved 95.00% accurate result, whereas the accuracy observed by Random Forest was 90.83%.
Energy prediction models are used in buildings as a performance evaluation engine in advanced control and optimisation, and in making informed decisions by facility managers and utilities for enhanced energy efficiency. Simplified and... more
Energy prediction models are used in buildings as a performance evaluation engine in advanced control and optimisation, and in making informed decisions by facility managers and utilities for enhanced energy efficiency. Simplified and data-driven models are often the preferred option where pertinent information for detailed simulation are not available and where fast responses are required. We compared the performance of the widely-used feed-forward back-propagation artificial neural network (ANN) with random forest (RF), an ensemble-based method gaining popularity in prediction – for predicting the hourly HVAC energy consumption of a hotel in Madrid, Spain. Incorporating social parameters such as the numbers of guests marginally increased prediction accuracy in both cases. Overall, ANN performed marginally better than RF with root-mean-square error (RMSE) of 4.97 and 6.10 respectively. However, the ease of tuning and modelling with categorical variables offers ensemble-based algorithms an advantage for dealing with multi-dimensional complex data, typical in buildings. RF performs internal cross-validation (i.e. using out-of-bag samples) and only has a few tuning parameters. Both models have comparable predictive power and nearly equally applicable in building energy applications.
Building energy consumption prediction has been a major concern in the recent years. The energy consumption in building is essential for building manager to manage the building energy management system (BEMS) and energy operator in making... more
Building energy consumption prediction has been a major concern in the recent years. The energy consumption in building is essential for building manager to manage the building energy management system (BEMS) and energy operator in making energy decision services. This paper proposes a building energy consumption prediction with novel pseudo dynamic transitional methods with occupancy profiles and operational power characteristics of buildings. The case study is applied to heating energy consumption of buildings and results show that pseudo dynamic model have coefficient of correlation of energy consumption of error of 0.02%. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational power level of building energy consumption.
This paper introduces a new approach for the prediction of hourly energy consumption in buildings. The proposed method uses nonlinear timeseries analysis techniques for the recon- struction of energy consumption timeseries and the... more
This paper introduces a new approach for the prediction of hourly energy consumption in buildings. The proposed method uses nonlinear timeseries analysis techniques for the recon- struction of energy consumption timeseries and the estimation of the dynamic invariants, and ar- tificial neural networks as a nonlinear modeling tool. Among the several neural network modeling factors that affect time-series prediction, the most important are the window-size and the sampling lags for the data. Relevant theoretical results related to the reconstruction of a dy- namical system are analyzed and the relation- ship between a correct embedding dimension and network performance is investigated. The problem is examined initially for the univariate case and is extended to include addi- tional calendar parameters, in the process of es- timating the optimum model. Different network topologies are considered, as well as existing approaches for solving multi- step ahead prediction problems. The predic...
Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socioeconomic to meet the strict CO 2 emission targets. This paper builds upon our previous work on the effect of... more
Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socioeconomic to meet the strict CO 2 emission targets. This paper builds upon our previous work on the effect of household transition on residential energy consumption where we developed a 3D urban energy prediction system (EvoEnergy) using the old UK panel data survey, namely, the British household panel data survey (BHPS). In particular, the aim of the present study is to examine the validity and reliability of EvoEnergy under the new UK household longitudinal study (UKHLS) launched in 2009. To achieve this aim, the household transition and energy prediction modules of EvoEnergy have been tested under both data sets using various statistical techniques such as Chow test. The analysis of the results advised that EvoEnergy remains a reliable prediction system and had a good prediction accuracy (MAPE 5%) when compared to actual energy performance certificate data. From this premise, we recommend researchers, who are working on data-driven energy consumption forecasting, to consider merging the BHPS and UKHLS data sets. This will, in turn, enable them to capture the bigger picture of different energy phenomena such as fuel poverty; consequently, anticipate problems with policy prior to their occurrence. Finally, the paper concludes by discussing two scenarios of EvoEnergy development in relation to energy policy and decision-making.
Extensive research works have been carried out over the past few decades in the development of simulation tools to predict the thermal performance of buildings. These validated tools have been used in the design of the building and its... more
Extensive research works have been carried out over the past few decades in the development of simulation tools to predict the thermal performance of buildings. These validated tools have been used in the design of the building and its components. However, limited simulation tools have been developed for modeling of district energy systems, which can potentially be a very laborious and time-consuming process. Besides many associated limitations, providing a realistic demand profile of the district energy systems is not a straightforward task due to high number of parameters involved in predicting a detail demand profile. This paper reports the development of a simplified model for predicting the thermal demand profile of a district heating system. The paper describes the method used to develop two types of simplified models to predict the thermal load of a variety of buildings (residential, office, attached, detached, etc.). The predictions were also compared with those made by the detailed simulation models. The simplified model was then utilized to predict the energy demand of a variety of districts types (residential, commercial or mix), and its prediction accuracy was compared with those made by detailed model: good agreement was observed between the results.
Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socioeconomic to meet the strict CO 2 emission targets. This paper builds upon our previous work on the effect of... more
Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socioeconomic to meet the strict CO 2 emission targets. This paper builds upon our previous work on the effect of household transition on residential energy consumption where we developed a 3D urban energy prediction system (EvoEnergy) using the old UK panel data survey, namely, the British household panel data survey (BHPS). In particular, the aim of the present study is to examine the validity and reliability of EvoEnergy under the new UK household longitudinal study (UKHLS) launched in 2009. To achieve this aim, the household transition and energy prediction modules of EvoEnergy have been tested under both data sets using various statistical techniques such as Chow test. The analysis of the results advised that EvoEnergy remains a reliable prediction system and had a good prediction accuracy (MAPE 5%) when compared to actual energy performance certificate...
