PEMODELAN IDENTIFIKASI PEMAKAIAN BEBAN SOLAR PANEL BERBASIS ARTIFICIAL NEURAL NETWORK

PEMODELAN IDENTIFIKASI PEMAKAIAN BEBAN SOLAR PANEL BERBASIS ARTIFICIAL NEURAL NETWOR

Electricity consumption pattern disaggregation based on user utilization factor

Non-Intrusive Appliance Load Monitoring (NIALM) technique has been studied intensively by many researchers to estimate the electricity consumption of each appliance in a monitored building. However, the method requires a detailed, secondby- second power consumption data which is commonly not available without the use of high specification energy meter. The common energy meter used in buildings can only capture low frequency data such as kWh for every thirty minutes. This thesis proposes a bottom-up approach for disaggregating kWh consumption of a building. The relationship between the load profile of a building and electricity usage pattern of the occupants were studied and analysed. From the findings, a method based on utilization factor that relates user usage pattern and kWh electricity consumption was proposed to perform load disaggregation. The method was applied on the practical kWh profile data of electricity consumption of Block P19a, Fakulti Kejuruteraan Elektrik, Universiti Teknologi Malaysia. The disaggregated kWh consumption results for air-conditioning and lighting system were validated with the actual kWh consumption recorded at the respective branch circuits of the building. Results from the analysis showed that the proposed method can be used to disaggregate energy consumption of a commercial building into air-conditioning and lighting systems. The proposed method could be extended to disaggregate the energy consumption for different areas of the building

Non-intrusive load management system for residential loads using artificial neural network based arduino microcontroller

The energy monitoring is one of the most important aspects of energy management. In fact there is a need to monitor the power consumption of a building or premises before planning technical actions to minimize the energy consumption. In traditional load monitoring method, a sensor or a group of sensors attached to every load of interest to monitor the system, which makes the system costly and complex. On the other hand, by Non-Intrusive Load Monitoring (NILM) the aggregated measurement of the building’s appliances can be used to identify and/or disaggregate the connected appliances in the building. Therefore, the method provides a simple, reliable and cost effective monitoring since it uses only one set of measuring sensors at the service entry. This thesis aims at finding a solution in the residential electrical energy management through the development of Artificial Neural Network Arduino (ANN-Arduino) NILM system for monitoring and controlling the energy consumption of the home appliances. The major goal of this research work is the development of a simplified ANN-based non-intrusive residential appliances identifier. It is a real-time ANN-Arduino NILM system for residential energy management with its performance evaluation and the calibration of the ZMPT101B voltage sensor module for accurate measurement, by using polynomial regression method. Using the sensor algorithm obtained, an error of 0.9% in the root mean square (rms) measurement of the voltage is obtained using peak-peak measurement method, in comparison to 2.5% when using instantaneous measurement method. Secondly, a residential energy consumption measurement and control system is developed using Arduino microcontroller, which accurately control the home appliances within the threshold power consumption level. The energy consumption measurement prototype has an accurate power and current measurement with error of 3.88% in current measurement when compared with the standard Fluke meter. An ANN-Arduino NILM system is also developed using steady-state signatures, which uses the feedforward ANN to identify the loads when it received the aggregated real power, rms current and power factor from the Arduino. Finally, the ANN-Arduino NILM based appliances’ management and control system is developed for keeping track of the appliances and managing their energy usage. The system accurately recognizes all the load combinations and the load controlling works within 2% time error. The overall system resulted into a new home appliances’ energy management system based on ANN-Arduino NILM that can be applied into smart electricity system at a reduced cost, reduced complexity and non-intrusively

Analysis and techniques for non-intrusive appliance load monitoring.

The increased public awareness of energy conservation and the demand for smart metering system have created interests in home energy monitoring. Load disaggregation using a single sensing point is considered a cost-effective way to sense individual appliance operation as opposed to using dedicated sensors for appliance monitoring. The aim of this thesis is to investigate the effectiveness of the analysis methods and techniques used in load disaggregation using a single point sensing. Time-frequency analysis methods such as Wavelet transforms are carefully examined and machine learning classifiers are used to develop the appropriate prediction models. The results have shown that the use of different Wavelet functions can significantly affect the classification accuracy. Among the four wavelets investigated in this thesis, two wavelets (Daubechies and Symlets) are able to provide the highest mean classification accuracy

An improved energy management methodology for the mining industry.

The focus for this work was the development of an improved energy management methodology tailored for the mining sector. Motivation for this research was driven by perception of slow progress in adoption of energy management practices to improve energy performance within the mining sector. Energy audits conducted for an underground mine, a mineral processing facility, and a pyrometallurgical process were reviewed and recommendations for improved data gathering, reporting and interpretation were identified. An obstacle for conducting energy audits in mines without extensive sub-metering is a lack of disaggregated data indicating end use. Thus a novel method was developed using signal processing techniques to disaggregate the end-use electricity consumption, exemplified through isolation of a mine hoist signal from the main electricity meter data. Further refinements to the method may lead to its widespread adoption, which may lower energy auditing costs via a reduced number of meters and infrastructure, as well as lower data storage requirements. Mine ventilation systems correspond to the largest energy demand center for underground mines. Thus a detailed analysis ensued with the development of a techno-economic model that could be used to assess various fan and duct options. Furthermore, the need for a standardized methodology for determination of duct friction factors from ventilation surveys was proposed, which included a method to verify the validity of the resulting value from asperity height measurements. A method was also suggested for determination of leakage and duct friction factor values from ventilation survey data. Dissemination of best practice is a strategy that could be employed to improve energy performance throughout the mining sector, thus a Best Practice database was developed to iv improve communication and provide a standardized reporting framework for sharing of energy conservation initiatives. Demonstration of continuous improvement is an underpinning element of the ISO 50001 energy management standard but as mines extract ore from deeper levels energy use increases. Thus ensued the development of a benchmarking metric, with the use of appropriate support variables that included mine depth, production, and climate data, that demonstrated the benefit of implemented energy conservation measures for an underground mine. The development of an ultimate energy management methodology for all stages of mineral processing from ‘Mine to Bullion’ is beyond the scope of this work. However, this research has resulted in several recommendations for improvement and identified areas for further improvements.Doctor of Philosophy (PhD) in Natural Resources Engineerin

Disaggregation von Haushaltsenergiemessdaten mit tiefen neuronalen Netzen

Die aktuell besten Ansätze zur Disaggregation von Haushaltsenergiemessdaten, die von handelsüblichen Smart Meter erfasst werden, basieren auf künstlichen neuronalen Netzen, die mit einer Deep-Learning-Methodik erstellt sind. Die Leistungsfähigkeit dieser Ansätze objektiv zu vergleichen ist allerdings schwer, da die Ansätze oft auf unterschiedlichen Datensätzen evaluiert werden, Trainingsverfahren nicht ausführlich beschrieben sind und keine einheitlichen Testmetriken verwendet werden. Erst durch die Evaluation bekannter Ansätze basierend auf einem einheitlichen Aufbau für Disaggregationsexperimente wird in dieser Arbeit deutlich, dass die Praxistauglichkeit aller Ansätze insbesondere durch die geringe Anzahl unterschiedlicher Gerätemodelle im Trainingsdatensatz beschränkt ist. Um für einen festgelegten Trainingsdatensatz den Fehler bei der Gerätelastgangsschätzung dennoch zu verringern, fokussiert sich die vorliegende Arbeit auf das Problem, dass Ansätze oftmals eindeutig falsche und unplausible Gerätelastgänge ausgeben, die von realen Geräten nicht reproduziert werden können. Dazu werden zwei verschiedene neue Ansätze untersucht, die die Plausibilität der geschätzten Lastgänge sicherstellen sollen. Zur Erzeugung von plausiblen Gerätelastgängen werden unterschiedliche Teile eines Generative Adversarial Networks (GAN) verwendet. Ein dritter Ansatz entwirft ein bestehendes Netzmodell neu und kombiniert dieses mit der U-Net-Architektur durch das Hinzufügen von Querverbindungen zwischen Netzschichten. Dies soll helfen, Detailinformationen in den Lastgängen besser zu reproduzieren. Bei der Evaluation der eigenen Ansätze mit dem gleichen Experimentenaufbau werden bei dem zweiten Ansatz häufiger realisierbare Lastgänge ausgegeben. Dabei bleibt die Disaggregationsgenauigkeit auf dem gleichen Niveau. Durch einen weiteren Austausch der beim Modelltraining verwendeten Verlustfunktion wird erreicht, dass sich alle betrachteten Bewertungsmetriken im Mittel über alle Geräte verbessern. Zudem kann bei bestimmten Geräteklassen mit der im dritten Ansatz evaluierten U-Net-Architektur eine weitere Verbesserung der Bewertungsmetriken erzielt werden