Electricity consumption forecasting models for administration buildings of the UK higher education sector

Amber, Khuram Pervez; Aslam, Muhammad Waqar; Hussain, Syed Kashif

doi:10.1016/j.enbuild.2015.01.008

Cited by 117 publications

(63 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regression models make use of linear functions to construct relationships between the dependent variables and numerous independent variables, including weather [8], income [9], GDP (Gross Domestic Product), and seasonal variables [10]. The validity and correctness of regression models have been confirmed by some empirical studies [11,12].…”

Section: Introductionmentioning

confidence: 97%

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

2018

View full text Add to dashboard Cite

Abstract:The energy management of office buildings has been a rising concern for owners, researchers, and energy suppliers. The volatility of power load in office buildings threatens energy consumption and risks device security. This paper investigates the load fluctuation patterns in an office building based on user data, using recurrence interval analysis for different thresholds. The recurrence intervals of volatility are fitted by stretched exponential distribution, from which the probability density function is derived. Then, the short-term and long-term memory effect on the fluctuations are learned by conditional probability density function and multifractal detrended fluctuation analysis, respectively. A hazard function is further established to analyze the risk estimation of load volatility and derive the value at risk (VaR). Thus, a functional relationship has been established between average recurrence interval and threshold. The methodology and analysis results addressed in this paper help to understand load fluctuation patterns and aid in the design of energy consumption strategies in office buildings. According to the results of our research, conclusions and management suggestions are provided at the end of this paper.

show abstract

Section: Introductionmentioning

confidence: 97%

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

2018

View full text Add to dashboard Cite

show abstract

“…This declining trend in carbon emissions certainly reflects the overall efforts made by the universities aiming to achieve their carbon-emissions reduction targets. However, despite such efforts and a number of ambitious initiatives, the English universities are far behind achieving their 2020 emissions reduction targets [6].…”

Section: Yearmentioning

confidence: 99%

“…A number of studies [7,[27][28][29][30][31][32][33][34][35] have investigated the effect of weather changes on building energy consumption. Amber, et al [6] investigated the effect of four weather variables, i.e., surrounding temperature, global irradiance, humidity and wind velocity on the electricity usage of different buildings. Among the four variables the surrounding temperature found to be the critical parameter which drives the building energy consumption.…”

Section: Weather Datamentioning

confidence: 99%

“…Nearly 68% space of a typical university campus in England is occupied by two major building categories, i.e., academic (42%) and administrative (26%) buildings [6]. In 2014/15, non-residential buildings of English universities consumed about 80% of total energy consumption where as 20% electricity consumption occurred in the sector's residential buildings [4].…”

Section: Yearmentioning

confidence: 99%

See 1 more Smart Citation

Energy Consumption Forecasting for University Sector Buildings

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

“…A short-term prediction model of building energy consumption based on artificial neural networks (ANNs) and a Bayesian regularization algorithm was developed in [4], and the influence of the parameters such as the delay and the number of hidden neurons was discussed carefully. In addition to the conventional factors such as temperature, the heat island effect as an unconventional factor has also been considered in [5][6][7][8] to improve the accuracy of the prediction. Although these methods above perform well, the following problems should be considered further: (1) Good prediction results mostly depend on a large amount of historical electricity data; however, most users cannot provide enough data; (2) The assumption of the prediction error with a Gaussian distribution is usually used in most of the intelligent algorithms, but it is inconsistent with the diversity of true prediction errors; (3) The prediction results are easily influenced by the type and characteristics of the data.…”

Section: Introductionmentioning

confidence: 99%

Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion

Duan

Qiu

et al. 2018

Entropy

View full text Add to dashboard Cite

Abstract:In recent years, with the deepening of China's electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model.

show abstract

Electricity consumption forecasting models for administration buildings of the UK higher education sector

Cited by 117 publications

References 18 publications

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

Energy Consumption Forecasting for University Sector Buildings

Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion

Contact Info

Product

Resources

About