Predicting Energy Usage Using Historical Data and Linear Models

Safa, Majeed; Allen, Jeremy J.; Safa, Mahdi

doi:10.22260/isarc2014/0070

Cited by 7 publications

(8 citation statements)

References 25 publications

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“…It is easy to develop and implement [38] and is widely used in the prediction of energy use. For example, Safa et al [39] presented a method to predict energy use in office buildings for the purpose of energy auditing. The study showed the capacity of simple models where the final regression model was based on outdoor temperature and occupancy with a monthly resolution.…”

Section: Energy Prediction Methodsmentioning

confidence: 99%

“…Catalina et al [40] developed a regression model for predicting the monthly space heating demand for residential buildings while another approach developed a generic equation of three variables for predicting the heating demand in apartments blocks [41]. The MLR method has also been applied with success in energy forecasting for swimming pool buildings [38,39].…”

Section: Energy Prediction Methodsmentioning

confidence: 99%

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The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway

et al. 2021

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This paper presents a statistical model for predicting the time-averaged total power consumption of an indoor swimming facility. The model can be a powerful tool for continuous supervision of the facility’s energy performance that can quickly disclose possible operational disruptions/irregularities and thus minimize annual energy use. Multiple linear regression analysis is used to analyze data collected in a swimming facility in Norway. The resolution of the original training dataset was in 1 min time steps and during the investigation was transposed both by time-averaging the data, and by treating part of the dataset exclusively. The statistically significant independent variables were found to be the outdoor dry-bulb temperature and the relative pool usage factor. The model accurately predicted the power consumption in the validation process, and also succeeded in disclosing all the critical operational disruptions in the validation dataset correctly. The model can therefore be applied as a dynamic energy benchmark for fault detection in swimming facilities. The final energy prediction model is relatively simple and can be deployed either in a spreadsheet or in the building automation reporting system, thus the method can contribute instantly to keep the operation of any swimming facility within the optimal individual energy performance range.

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Section: Energy Prediction Methodsmentioning

confidence: 99%

Section: Energy Prediction Methodsmentioning

confidence: 99%

The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway

et al. 2021

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“…Specifically, two different statistical models are used in this study. These models are multiple linear regression model [12][13][14] and Box-Jenkins' autoregressive model of order 1 [15][16][17][18][19]. It also provides the source and method of data collection.…”

Section: Methodsmentioning

confidence: 99%

Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1

Ebukanson¹,

Chidi²,

Iriaoghuan³

2017

IJEPE

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Abstract:This study presents statistical analysis of electricity generation in Nigeria using two different statistical models, namely; multiple linear regression model and box-Jenkins' autoregressive model of order 1. Two climatic variables (rainfall and temperature) were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. Furthermore, correlation between power generation and the two climatic variables (rainfall and temperature), were carried out and the result reveals that the amount of rainfall has significant and positive relationship with power generation in Nigeria. Specifically, rainfall has correlation value of r = 0.927 with the power generation at probability, p = 0.000 and the relationship was significant at 1% (p<0.01). However, temperature although it is positively correlated, does not significantly affect power generation. Temperature has correlation value of t = 0.136 with power generation at probability, p = 0.658 (p>0.05) and the relationship was significant at 5% (p<0.05). Among the two statistical models, multiple linear regression model was selected as the best model as it gave the highest value of coefficient of determination (r 2 =99.77%) and the least Root Mean Square Error (60.27%).

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“…This model's coefficient of determination (R 2 ), the indication of the goodness of fit, was 0.7263, with all variables significantly contributing to the model. However, when the residual plot for this model was examined it showed a clear pattern that suggested the possibility of the need for variable transformation [19].…”

Section: Forecasting With Linear Modelingmentioning

confidence: 99%

“…This final linear model was chosen to be more successful over other non-linear methods as it had the highest R 2 value (0.7711), which indicates high correlation between predicted and the actual observed values. The other comparison metric that was used to evaluate this model against others was root mean squared error (RMSE) (2219.7), which measures the standard deviation of the model's prediction error, and thus a lower value is more desirable [19]. One of the results of creating a linear model is that the coefficients of the model indicate the relationship of each feature to the response variable.…”

Section: Forecasting With Linear Modelingmentioning

confidence: 99%

Forecasting energy trends and peak usage at the University of Virginia

Heylman

Kim

Wang

2015

2015 Systems and Information Engineering Design Symposium

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Forecasting energy trends, especially peak usage, is a valuable and necessary part of energy management. Accurate prediction allows for the control and alleviation of overuse during peak times with the implementation of energy efficiencies. Using hourly kilowatt data from over 200 buildings on the University of Virginia's campus this paper examines the most effective techniques for developing both individual building and overall grid use energy models with a specific focus on predetermining peak usage points. This paper proposes that the expectation-maximization algorithm within the state-space framework is the most effective method for smoothing missing values, a common occurrence when working with metered energy data. Next, this paper covers two separate methods for creating forecasting models. The first, a linear model, was found to be most successful in predicting campuswide energy usage, with the inclusion of features of temperature, humidity, school session, and three temporal variables. The second method, found to be most successful when forecasting short term individual building energy use, uses a seasonal autoregressive integrated moving average (SARIMA) model. Finally this paper delves into the intricacies involved in clustering buildings based on their energy usage trends rather than building use, and the implications that arise from such a process. The conclusions made in this paper can be rescaled and applied to larger energy systems outside the university setting.

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Predicting Energy Usage Using Historical Data and Linear Models

Cited by 7 publications

References 25 publications

The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway

The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway

Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1

Forecasting energy trends and peak usage at the University of Virginia

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