Air conditioning system is a complex system and consumes the most energy in a building. Any fault in the system operation such as cooling tower fan faulty, compressor failure, damper stuck, etc. could lead to energy wastage and reduction in the system’s coefficient of performance (COP). Due to the complexity of the air conditioning system, detecting those faults is hard as it requires exhaustive inspections. This paper consists of two parts; i) to investigate the impact of different faults related to the air conditioning system on COP and ii) to analyse the performances of machine learning algorithms to classify those faults. Three supervised learning classifier models were developed, which were deep learning, support vector machine (SVM) and multi-layer perceptron (MLP). The performances of each classifier were investigated in terms of six different classes of faults. Results showed that different faults give different negative impacts on the COP. Also, the three supervised learning classifier models able to classify all faults for more than 94%, and MLP produced the highest accuracy and precision among all.
This paper investigates the hot spot temperature of transformer thermal model due to unbalanced harmonic loads from the network. The finite element method has been used to solve the coupling multiphysic for heat transfer in solid and fluid. All material properties in the model were been took into consideration such as copper as the coil material, iron as the core material and transformer oil as the coolant material for the transformer. The transient study on the model has been set for 1minutes using 30 degree celcius as the ambient temperature reference. The simulation hot spot temperature result has been compared for rated load (without harmonic) versus the unbalanced load (with harmonic) which shown in 2D regime. It can be clearly seen the significant increment of the hotspot temperature of the transformer from the rated load to the unbalanced harmonic load. The result has successfully shows the detection of the prospect failure of the transformer due to the harmonic current load in a form of winding loss that contributes to the hotspot temperature of the transformer.
Many air-conditioning (AC) systems are designed to operate at maximum cooling capacity regardless of the variation in the daily cooling load. At low loads, the conditions can be uncomfortably cold and the overcooling is an unnecessary waste of energy. To address these two issues, a multiple refrigeration circuit concept is proposed and applied to a roof-top bus AC system. A two-circuit model is proposed for a standard bus size in which each circuit has two evaporators of equal sizes arranged in parallel and installed on each passenger row, respectively. This means that each passenger row is served by two different evaporators sharing a common heat exchanger box. Depending on the cooling load, this concept allows one or both circuits (compressor motors) to be switched on and during either modes, it also allows one or more sets of evaporator blowers to be switched on. A steady-state computer model has been developed to simulate the performance of the proposed two-circuit AC system. A two-circuit air conditioner is also designed to form a roof-top bus AC system, fabricated, and installed on to an experimental rig. The experimental data are used to validate the computer model. The validation is on the system thermal performance and on the evaporator air outlet conditions (dry bulb temperature and relative humidity) at different modes of system operation, either at full or partial cooling loads. The simulated results gave satisfactory agreement with those obtained from the experimental work. Maximum absolute deviations are within the range of 19.3 per cent, although most of the simulated results are less than a 10 per cent range from the experimental ones, which validates the computer program. The paper describes the modelling work carried out and the results obtained are presented in comparison with the experimental data.
An industrial energy analysis was conducted to identify the local energy pattern for every industrial sector and the improvement that can be undertaken on energy consumption by the industry in Malaysia. A Questionnaire was distributed to the FMM members. Energy management awareness among the respondent were fairly high where the majority of the large scale companies have their own Energy Management Team (EMT) and the majority of the SMI do not have their EMT. Most companies have introduced energy saving measures in their operations of which most of the current and future activities are in the area of thermal system, energy auditing, instrumentation and building envelope. Lack of relevant information on energy technology was the main obstacle to the implementation of energy conservation program in these companies. Other problems identified according to priority were costing, technology, manpower, government measures and supply of energy. The energy consumed by the industries are derived from diesel, heavy and light fuel oil. LPG, electricity and others (including coal, rubber wood chips, petrol, etc). The overall energy consumptions composed of 12% diesel, 33% light fuel oil, 7% heavy fuel oil, 2% LPG, 0.01% electricity and 46% of other energy sources. Energy audit, conducted on 10 factories of various sectors, identified the potential energy conservation measures. These measures were recommended to the management of the individual factory or their technical staff for economic feasibility studies. Finally, recommendation were made for the government to play an important role in guiding, informing and monitoring the energy usage through relevant policies and the setting up of a center for energy studies.
Accurate load forecasting is an important element for proper planning and management of electricity production. Although load forecasting has been an important area of research, methods for accurate load forecasting is still scarce in the literature. This paper presents a study on a hybrid load forecasting method that combines the Least Square Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) methods for building load forecasting. The performance of the LSSVM-ABC hybrid method was compared to the LSSVM method in building load forecasting problems and the results has shown that the hybrid method is able to substantially improve the load forecasting ability of the LSSVM method.
In conducting load forecasting, the accuracy of forecasting is an important aspect in planning and managing electricity. Thus, a new hybrid model is presented in this paper, which combines the Group Method of Data Handling, Least Square Support Vector Machine and Artificial Bee Colony (GLSSVM- ABC) for building load forecasting. Its performance accuracy has been compared with other methods by using the Mean Absolute Percentage Error (MAPE) and Root Means Square Error (RMSE). It was found that the proposed method has resulted in better performance accuracy in terms of both MAPE and RMSE. The MAPE analysis showed an increase in performance accuracy of more than 7 percent when compared to other methods. The RMSE analysis showed an increase in performance accuracy of more than 5 percent when compared to other methods. The results in this study showed that the proposed method is proven to be effective and has great potential for accurate building load forecasting.
This paper presents an improved building load forecasting method using a combined Least Square Support Vector Machine and modified Artificial Bee Colony. The main contribution of the proposed method is the improvement in the exploitation capability of the standard Artificial Bee Colony, in which a different probability selection has been introduced. This was achieved by changing the standard probability selection with the clonal selection algorithm. The results from two other methods were compared with the results from the proposed method to validate the performance of the proposed forecasting method. The accuracy of the proposed method was evaluated using the Mean Absolute Error, Mean Absolute Percentage Error and Root Mean Square Error. It was found that the proposed method had improved the accuracy by more than 50 % compared to the other methods. The results of the study showed that the proposed method has great potential to be used as an accurate forecasting method.
Fuzzy logic controller has been proven to control nonlinear process system and HVAC is a type of nonlinear process systems. This paper studies the performance of fuzzy logic controller with three and five term membership function in centralized chilled water system. Three different cases are simulated and analyzed for both type of controllers. Results show that the performances between both controllers are almost similar with no significant difference. It is also encountered that in certain cases, 3-mf fuzzy logic controller outperformed 5-mf fuzzy logic controller.
Energy consumption in commercial buildings contributes greatly towards high energy demand, especially in developed countries. For this reason, energy efficiency in buildings is given increasing attention and included in the energy policy at all levels. However, the complexities of building's energy system make it difficult to gather information on building energy consumption. Many methods have been proposed to measure the energy consumption accurately. The major issues are how to regularly monitor the performance of energy management programs and how to reduce energy usage in a building. This paper focuses on the Energy-Efficiency Index (EEI) as an indicator to track the performance of energy consumption in buildings. A study is conducted at the Engineering Faculties of Universiti Teknologi Malaysia (UTM) using a combination of standard measurements of energy consumption and air-conditioned area from building's databases. Various energy saving management programs were conducted in these faculties since 2010. The results from these programs showed a reduction in EEI and provide opportunities for continuous energy saving practices.
'/%3e%3cuse xlink:href='%23a' transform='translate(0 -400)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -600)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -800)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -1000)'/%3e%3cuse xlink:href='%23a' transform='translate(0 -1200)'/%3e%3cpath d='M0 0h1400v800H0z' style='fill:%23010066'/%3e%3cpath d='M576 100c-166.146 0-301 134.406-301 300s134.854 300 301 300c60.027 0 115.955-17.564 162.927-47.783-27.353 9.44-56.71 14.602-87.271 14.602-147.327 0-266.897-119.172-266.897-266.01 0-146.837 119.57-266.01 266.897-266.01 32.558 0 63.746 5.815 92.602 16.468C696.217 118.91 638.305 100 576 100z' style='fill:%23fc0'/%3e%3cpath d='m914.286 471.429-99.538-53.25 29.43 108.982-66.575-91.165-20.77 110.96-20.428-111.024-66.857 90.96L699.314 418l-99.701 52.943 74.065-85.192-112.8 4.441 103.694-44.62-103.555-44.94L673.8 305.42 600 220l99.538 53.25-29.43-108.982 66.575 91.165 20.77-110.96 20.428 111.023 66.857-90.959L814.97 273.43l99.702-52.944-74.065 85.193 112.8-4.441-103.694 44.62 103.555 44.94-112.785-4.79z' style='fill:%23fc0' transform='matrix(1.2738 0 0 1.2423 -89.443 -29.478)'/%3e%3c/svg%3e)
'/%3e%3cpath fill='%23fff' d='m8.751-17.959 10.11 11.373L3.997-9.844l13.94-6.1-7.692 13.129z'/%3e%3c/svg%3e)
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)