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Practical Examples of Energy Optimization Models

Nguyễn Gia Hào

Academic year: 2023

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Mohd Faris Abdullah Jabatan Kejuruteraan Elektrik dan Elektronik Universiti Teknologi Petronas Seri Iskandar, Perak, Malaysia Ramani Kannan. Jabatan Kejuruteraan Elektrik dan Elektronik Ramani Kannan, Universiti Teknologi PETRONAS, Seri Iskandar, Perak Darul Ridzuan, Malaysia.

1 Introduction

This will overcome the weakness of the existing method to calculate the dispersion for each solar radiation data.

2 Construction General Fuzzy Regression

Fuzzy Regression

In the above equations, matricesy and ˜X are the data matrices associated with response variables and predictor variables, respectively. To obtain the regression parameters, Eq. where Xi is the transposed matrix of The regression coefficients can be derived by matrix operations as follows: is the inverse matrix ofXXwhere,.

Fig. 2 Symmetrical Fuzzy regression coefficient
Fig. 2 Symmetrical Fuzzy regression coefficient

Fuzzy Quadratic Regression Equation and Error Calculation

3 Data Collection

4 Results and Discussion

It is clear that with new distribution value,cn,j, the fuzzy regression model is better than standard polynomial quadratic fitting (crisp). The result of the proposed fuzzy regression model can also be further improved by using both fuzzy regression i.e.

Table 2 Spreading value
Table 2 Spreading value

5 Prediction Global Solar Radiation

When the value of the free parameter is −1.5 (negative) as well as 1.5 (greater than 1), the fit is not good and this is the main reason why the best fitting model can be obtained when it is between 0 and 1.

6 Summary

Numerical simulation using the new spreading value gave a better model compared to standard polynomial quadratic fitting, i.e. Based on the results, soft regression with a =0.3 gives the best solar radiation fit and is used to predict the amount of solar radiation for every 15 minutes between 1100 and 1600 h.

Karim SAA, Singh BSM, Karim BA, Hasan MK, Sulaiman J, Janier Josefina B, Ismail MT (2012) Denoising data radiasi matahari menggunakan wavelet Meyer. Jalil MAA, Karim SAA, Baharuddin Z, Abdullah MF, Othman M (2018) Peramalan data radiasi matahari menggunakan metode Gaussian dan polynomial fitting.

Performance Evaluation of a 2-kWp Grid Connected Solar Photovoltaic System


However, there are some obstacles to the implementation of renewable energy in Malaysia, which can be classified into three divisions: In [2], authors discussed about the energy demand in rural island areas, the applications of hybrid electricity generation systems, especially in the aspects of renewable energy , the pros and cons of renewable energy, environmental issues and a country's economic constitutions.

2 Solar Energy

Solar Thermal

Solar thermal technology has been used by man since a long time ago, mainly for domestic use such as space heating or water heating.

Solar PV

3 Solar Energy in Malaysia

This is one of the ways to stimulate the growth of renewable energy in Malaysia [4]. To keep up with the pace of the world, there are some financial assistance facilities provided by the GoM to stimulate the development of sustainable energy technology in Malaysia.

4 Solar PV Performance Indicators

  • Energy Output
  • Array Yield
  • Final Yield
  • Reference Yield
  • Solar PV Module Efficiency
  • Inverter Efficiency
  • Performance Ratio
  • Capacity Factor

EDC to the product of the total daily irradiation in the plane, HT and the surface of the PV array, Am [9]. EDC daily array DC energy yield HT total daily irradiation in the plane Am surface of the PV array. The performance ratio is the ratio between the actual generation data of the solar PV system and the total solar radiation received [10].

5 Data Collection

The period of this study was selected from September 2017 to August 2018 for one year of data collected from the data logger of a 2 kWp grid-connected solar PV system at port 3 UTP. The collected data are the output energy of the solar PV module DC, the output energy of the AC inverter, the solar radiation and the temperature of the PV module.

6 Data Analysis

7 Solar PV Performance

The monthly efficiency of the solar PV module and the inverter are almost constant as shown in Figure 4. The monthly performance ratio and performance factor of the grid-connected solar PV system are shown in Figure 6. 9 DC and AC power outputs, solar irradiance and PV temperature module on February 25, 2018.

Fig. 3 Monthly DC and AC energy outputs and solar irradiation
Fig. 3 Monthly DC and AC energy outputs and solar irradiation

8 Summary

In general, the grid-connected solar PV system received the highest amount of solar radiation around 10.00 am to 3.00 pm every day. 33 radiation received by the grid-connected solar PV system that was affected by the weather. The monthly performance ratio and capacity factor of the grid-connected solar PV system were within the accepted values ​​for the tropical climate installation.

Power Consumption Optimization for the Industrial Load Plant Using

Increasing ambient temperature (TAMB) and humidity (RH), increases the demand for cooling and results in higher energy consumption [6,7]. Therefore, optimization techniques have the potential to save energy while maintaining cooling comfort in buildings.

2 Literature Review

Therefore, there is a need for the proposed approach to assess the total consumption of a cooling system with and without weather influence. Two model approaches were developed and optimized based on adjusted cooling temperature to estimate power consumption. Section 3 presents the measured data and constructed the proposed approach by incorporating weather factors such as ambient temperature and ambient humidity.

3 The Proposed Methodology

  • Measured Data Identification
  • Architecture of ANFIS-APSO Model
  • The Performance of Computation Model
  • The Impact of Weather Data
  • Optimization Model by APSO Technique
  • Results
  • Discussion

Then the temperature prediction result in Eqs. 12) and (13) based on ANFIS-APSO1 and ANFIS-APSO2 were used to estimate the energy consumption in Eq. The power consumption can then be adjusted according to the cooling demand when the weather conditions are known. To evaluate the performance of the proposal, the root mean square error (RMSE)-based statistical analysis term is as,.

Fig. 1 Flow chart of research methodology
Fig. 1 Flow chart of research methodology

5 Conclusion

Ardakani AJ, Ardakani FF, Hosseinian SH (2008) A new approach for optimal radiator loading using particle swarm optimization. Kusiak A, Xu G, Tang FJE (2011) Optimization of an HVAC system with a strength multi-objective particle swarm algorithm. Karami M, Wang LJATE (2018) Particle swarm optimization for control operation of a water-cooled chiller with fully variable speed.

Cost Benefit Opportunity for End Use Segment Using Lighting Retrofit

Air conditioning system is considered as one of the most important loads of a commercial building, providing comfort cooling, especially in a country with a tropical climate like Malaysia. Lighting is also considered one of the most common loads and represents 20% of energy consumption in commercial buildings [6]. However, it is important that the lighting systems are designed appropriately so that they are a.

Fig. 1 Taylor’s University Lakeside Campus (https://goo.gl/maps/yx5QNmwPno6gpWzTA)
Fig. 1 Taylor’s University Lakeside Campus (https://goo.gl/maps/yx5QNmwPno6gpWzTA)

2 End Use Segment Model and Approach Used 2.1 Modelling of the System

Building Energy Retrofit

The application of the zoning method is used to evenly distribute the floor area to optimize the load rating. Therefore, by filtering the measured data, inadequate lux levels are highlighted as a possible lighting upgrade. In addition to measuring the floor area, choosing the type of lighting is also important.

Fig. 4 Lighting retrofit and temperature measurement
Fig. 4 Lighting retrofit and temperature measurement

Choice of Lighting Retrofit

In our case, we evenly distribute the area for each floor and divide them into 9 zones. However, the average lifespan of LEDs is 6 times CFL and 40 times incandescent. Using cost-benefit analysis, the payback period is determined to identify whether retrofitting lighting helps reduce power consumption and utility bill costs.

3 Data Capture and Cost Effective Analysis

The power factor of the four transformers are all kept at the 0.85 level above.

4 Cost Effective Lighting Retrofit Opportunities

So, the number of LEDs needed is calculated by finding the difference in lux for each zone. It is therefore the total number of LEDs required for retrofitting for 2 zones of the corridor area which is equal to 20. The total load reduction is calculated by multiplying the load reduction and the total number of lamps.

5 Summary

A cost benefit opportunity for the end-use segment that uses lighting. Stamdar d Lux−Measur ed Lux)×Ar ea. Therefore, the total annual energy savings for retrofitting the corridors is found to be RM668 and the payback period is 1.19 years. Table4(continued) CorridorClassroom(18W)Classroom(36W)Office(36W)Office(18W) No.Needed LEDsHallway AnabessNeeded areLEDs LEDFluorescent tubes Needed LEDsFluorescent tubes Needed Anabess LEDsNeeded LED pinsPLC Total savingsRM21,888.05 Payback period (years ).

A Study of Electrical Field Stress Issues in Commercial Power MOSFET

Power MOSFET offered many advantages over other semiconductor devices, especially for harsh environment application including space mission. Power MOSFETs are a cornerstone of space mission applications, making their reliability within the harsh space radiation environment imperative to mission success. Finally, the motivation of this present study is to investigate the effect of radiation on commercial power MOSFET using Sentaurus Synopsys software simulation [5-7].

2 The Topology of Space Radiation Environment

In the outer radiation belt, most of the ion is protons and an alpha particle [10-12]. Accumulation of the newly generated electron-hole pair imparts electrical stress to the device and will degrade the device performance. Heavy Kannan Single Ion generates a pair of new electron holes by depositing their high energy on the road.

3 Software Simulation

  • Gamma Ray Radiation
  • Particle Radiation
  • Gamma Ray
  • Particle Radiation

Figure 4 shows the device structure for the electric field in the device before and after irradiation with gamma ray irradiation at a dose rate of 25 MeV when 10 V voltage is applied to the gate. 7 Electric field in particle irradiation equipment; virgin equipment, post-irradiation equipment. In terms of electrical performance, a single heavy ion also makes a big impact on the device.

Fig. 1 Technical flowchart for the software simulation
Fig. 1 Technical flowchart for the software simulation

5 Conclusions

Schwank JR, Shaneyfelt MR, Fleetwood DM, Felix JA, Dodd PE, Paillet P et al (2008) Radiation effects in MOS oxides. Davidovi'c V, Dankovi'c D, Ili'c A, Mani'c I, Golubovi'c S, Djori'c-Veljkovi'c S et al (2016) NBTI and irradiation effects in p-channel VDMOS transistors. Privat A, Touboul AD, Michez A, Bourdarie S, Vaille J, Wrobel F et al (2014) On the use of results after irradiation port stress to refine the determination of the sensitive surgical area.

Time Series Models of High Frequency Solar Radiation Data

The high frequency data commonly used in financial time series where stock price information is tracked by investors every second. It also attracts the attention of many researchers to focus on empirical investigation of high-frequency data in financial and economic time series such as stock market index [1,2] and exchange rates [3]. Section 4 is devoted to results and discussion, while Section 5 finally concludes with a chapter summary.

2 The Literature Review of Related Work

They found that ARMA-GARCH models can better capture the behavior of solar radiation observations than ANNs. 11] studied the properties of time series of solar radiation data from ten stations in the USA. Based on previous literature, a new model is proposed for modeling high-frequency solar radiation data.

3 Data and Methodology


7] achieve by showing that ANN a machine learning model provides better prediction for daily global radiation data from affluent cities of Queensland, Australia. 12] conducted an empirical investigation on solar radiation series using Autoregressive Moving Average (ARMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH), ARMA-GARCH models using two data sets from China. 9], construct a seasonal/non-seasonal autoregressive integrated moving average (SARIMA/ARIMA) model to forecast daily and monthly solar radiation in Korea.

ARFIMA Models and Box and Jenkins Procedure

The Box and Jenkins procedure will then be applied to find the best model for the high-frequency solar radiation data. In this chapter, three datasets of high-frequency solar radiation data are obtained from the station in Putrajaya, Malaysia. Ozoegwu CG (2019) Artificial neural network prediction of global monthly average daily solar radiation of selected locations based on time series and month number.

Fig. 2 Distribution of the data
Fig. 2 Distribution of the data



Table 1 ANOVA table for testing the significance of polynomial regression Source of
Fig. 3 Result for spreading value using given formula of c n , j with μ = 0
Fig. 4 Fitting curves for spreading value using new c n , j with μ = 0
Figure 5 shows the examples of fitting curve using convex combination method with a = 0



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