Next-Day Daily Energy Consumption Forecast Model Development and Model Implementation

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Li Song ◽  
Ik-seong Joo ◽  
Subroto Gunawan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model Development ◽  
Thermal Storage ◽  
Forecast Model ◽  
Automation System ◽  
Peak Demand ◽  
Critical Set ◽  
Complete Control

Thermal storage systems were originally designed to shift on-peak cooling production to off-peak cooling production in order to reduce on-peak electricity demand. Recently, however, the reduction of both on- and off-peak demand is a critical issue. Reduction of on- and off-peak demand can also extend the life span and defer or eliminate the replacement of power transformers. Next day electricity consumption is a critical set point to operate chillers and associated pumps at the appropriate time. In this paper, a data evaluation process using the annual daily average cooling consumption of a building was conducted. Three real-time building load forecasting models were investigated: a first-order autoregressive model (AR(1)), an autogressive integrated moving average model (ARIMA(0,1,0)), and a linear regression model. A comparison of results shows that the AR(1) and ARIMA(0,1,0) models provide superior results to the linear regression model, except that the AR(1) model has a few unacceptable spikes. A complete control algorithm integrated with a corrected AR(1) forecast model for a chiller plant including chillers, thermal storage system, and pumping systems was developed and implemented to verify the feasibility of applying this algorithm in the building automation system. Application results are also introduced in the paper.

Minimizing On-Peak and Off-Peak Demands for a Thermal Storage System: Forecast Model Analysis to Predict Next Day Daily Average Load and Model Application

2010 ◽  
Author(s):  
Li Song ◽  
Ik-Seong Joo ◽  
Subroto Gunawan
Keyword(s):  
Random Walk ◽  
Storage System ◽  
Thermal Storage ◽  
Forecast Model ◽  
Electricity Demand ◽  
Random Walk Model ◽  
Automation System ◽  
Critical Set ◽  
Complete Control ◽  
Daily Average

Thermal storage systems were originally designed to shift on-peak cooling production to off-peak cooling production to reduce on-peak electricity demand. Recently, however, the reduction of both on- and off-peak demands is becoming an exceedingly important issue. Reduction of on- and off-peak demands can also extend the life span and defer or eliminate the replacement of power transformers due to potential shortage of building power capacity caused by anticipated equipment load increases. Next day daily average electricity demand is a critical set point to operate chillers and associated pumps at the appropriate time. For this paper, a mathematical analysis of the annual daily average cooling of a building was conducted, and three real-time building load forecasting models were developed: a first-order autoregressive model, a random walk model, and a linear regression model. A comparison of results shows that the random walk model provides the best forecast. A complete control algorithm integrated with forecast model for a chiller plant including chillers, thermal storage system and pumping systems was developed to verify the feasibility of applying this algorithm in the building automation system. Application results are introduced in this paper as well.

Characterizing the Conductivity of Aerosol Jet Printed Silver Features on Glass

2021 ◽  
Author(s):  
Dilan Ratnayake ◽  
Alexander Thomas Curry ◽  
Chuang Qu ◽  
John Usher ◽  
Kevin Walsh
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Narrow Range ◽  
Model Development ◽  
Post Processing ◽  
Printable Electronics ◽  
Wide Range ◽  
Jet Printing ◽  
Aerosol Jet Printing

Abstract Aerosol Jet Printing shows a lot of promise for the future of printable electronics. It is compatible with a wide range of materials and can be printed on nearly any type of surface features because of its 3–5 mm standoff distance from the substrate. However, nearly all materials printed require some form of post-sintering processing to reduce the electrical resistance. Many companies develop these materials, but only provide a narrow range of post processing results to demonstrate the achievable conductivity values. In this paper, a design of experiment (DOE) is presented that demonstrates a way to characterize any material for Aerosol Jet Printing during and after post sintering processing by measuring conductivity with different time and temperature values. From these results, a linear regression model can be made to develop an equation that predicts conductivity at a given time-temperature value. This paper applies this method to Clariant Ag ink and sinters silver pads in an oven. A linear regression model is successfully developed that fits the data very well. From this model, an equation is derived to predict the conductivity of the Clariant Ag ink for any time-temperature value. Although only demonstrated with an oven and one type of ink, this method of experimentation and model development can be done with any material and any post processing method.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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