Statistical Modeling for Real Domestic Hot Water Consumption Forecasting

Lomet, Aurore; Suard, Frédéric; Chèze, David

doi:10.1016/j.egypro.2015.02.138

Cited by 39 publications

(27 citation statements)

References 10 publications

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“…Among these, MR is a simple, reliable and a quick technique [11][12][13][14]. A number of researchers [11,12,[15][16][17][18][19][20][21][22][23][24] have used the MR method in their studies, but all such MR models forecast energy consumption of a single building or a region and require a lot of input data. Energy managers and their teams have always busy schedule and they would prefer a reliable and quick single model for different building categories instead of different forecasting models [24].…”

Section: Yearmentioning

confidence: 99%

Energy Consumption Forecasting for University Sector Buildings

et al. 2017

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Reliable energy forecasting helps managers to prepare future budgets for their buildings. Therefore, a simple, easier, less time consuming and reliable forecasting model which could be used for different types of buildings is desired. In this paper, we have presented a forecasting model based on five years of real data sets for one dependent variable (the daily electricity consumption) and six explanatory variables (ambient temperature, solar radiation, relative humidity, wind speed, weekday index and building type). A single mathematical equation for forecasting daily electricity usage of university buildings has been developed using the Multiple Regression (MR) technique. Data of two such buildings, located at the Southwark Campus of London South Bank University in London, have been used for this study. The predicted test results of MR model are examined and judged against real electricity consumption data of both buildings for year 2011. The results demonstrate that out of six explanatory variables, three variables; surrounding temperature, weekday index and building type have significant influence on buildings energy consumption. The results of this model are associated with a Normalized Root Mean Square Error (NRMSE) of 12% for the administrative building and 13% for the academic building. Finally, some limitations of this study have also been discussed.

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Section: Yearmentioning

confidence: 99%

Energy Consumption Forecasting for University Sector Buildings

et al. 2017

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“…To overcome these challenges, [17,20] used models that have multi-function prediction. Inspired by these models, this paper uses a time series decomposition forecasting methods, seasonal autoregressive integrated moving average (ARIMA) to forecast customer water demand pattern.…”

Section: Forecasting the Customer's Demand Patternmentioning

confidence: 99%

“…In addition, the residential hot water consumption pattern has a lot of uncertainty due to stochastic events such as guest visiting, vacations, etc., thus many studies affirmed the inability to model and predict domestic hot water consumption [18][19][20]. To overcome these challenges, [17,20] used models that have multi-function prediction.…”

Section: Forecasting the Customer's Demand Patternmentioning

confidence: 99%

Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

Lin

Xiao

2017

Energies

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This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.

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“…The objectives and the corresponding goals are given in Table 7. Regarding MOGA's design framework parameters specification, for experiments I and III, the range rd m , d M s, where d m and d M are the minimum and maximum number of features, was set to [1,30] while for experiments II and IV they were set to [1,15] and [1,21], respectively. Similarly, for experiments I and III, the range rn m , n M s, where n m and n M are the minimum and maximum number of neurons, was set to [2,30] while for experiments II and IV, these ranges were set to r1, 18s and r1, 21s, repectively.…”

Section: Design Experimentsmentioning

confidence: 99%

A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building

et al. 2016

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Abstract:Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naïve autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigación en Energía SOLar or CIESOL in Spanish) bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA) perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity.

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Statistical Modeling for Real Domestic Hot Water Consumption Forecasting

Cited by 39 publications

References 10 publications

Energy Consumption Forecasting for University Sector Buildings

Energy Consumption Forecasting for University Sector Buildings

Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building

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