Analysing the Residential Electricity Consumption using Smart Meter

A massive amount of electricity usage may be accessed on an everyday and hourly basis due to the advancement of smart power measuring technology. Electricity demand management and utility load management are made easier by energy usage forecasts. The majority of earlier studies have concentrated on the power consumption of business clients or residential buildings, or they have experimented with individual household electricity usage using behavioral and occupant sensor information. This study used smart meters to examine energy usage at a single household level to enhance residential energy services and gather knowledge for developing demand response strategies.The power usage of various appliances in a single household is estimated, by utilizing Autoregressive Integrated Moving Average (ARIMA) modeling technique, which is applied to daily, weekly, and monthly information granularity. To select the household’s energy consumption dataset for this study, a multivariate time-series dataset describing the four-year electricity usage of a household is provided. The use of Exploratory Data Analysis (EDA) is utilizedfor the selection of features and data visualization. The correlation coefficients with the daily usage of the household have been computed for the characteristics prepared for the forecast. The top three major determinants with the top three positive significance are "temperature," "hour of the day," and "peak index." A single household's usage is inversely related to the variables having negative coefficients. It should be noticed that the correlations among a household's attributes with usage vary from one another. Finally, the power prediction is analyzed in a single household

Forecasting the Usage of Household Appliances Through Power Meter Sensors for Demand Management in the Smart Grid

Electricity demand management mechanisms are expected to play a key role in smart grid infrastructures to reduce buildings power demand at peak hours, by means of dynamic pricing strategies. Unfortunately these kinds of mechanisms require the users to manually set a lot of configuration parameters, thereby reducing the usability of these solutions. In this paper we propose a system, developed within the BEE Project, for predicting the usage of household appliances in order to automatically provide inputs to electricity management mechanism, exactly in the same way a user could do. In our architecture we use a wireless power meter sensor network to monitor home appliances consumption. Data provided by sensors are then processed every 24 hours to forecast which devices will be used on the next day, at what time and for how long. This information represents just the input parameters required by load demand management systems, hence avoiding complex manual settings by the user

Optimization of Household Energy Management Based on the Simplex Algorithm

Small scale intermittent renewable energy consisting of roof-mounted photovoltaic generators and micro wind turbines for the household residential have been widely integrated into the power grid. Meanwhile, more and more home appliances are utilized, including schedulable and non-schedulable home appliances. With the deployment of smart technologies, the control strategies of home energy management system are developed and this provides the possibility to minimize the energy bill and improve the energy efficiency by scheduling the controllable home appliances without sacrificing preference of the customer. In this thesis, an optimization strategy based on the Simplex algorithm has been proposed. The target is to optimize the energy consumption in households by scheduling the household appliances, considering the day-ahead electricity price from Nord Pool market and the roof-mounted PV production. Firstly, the schedules are generated from the optimization algorithm and then interpreted to control the appliances to achieve energy bill saving. In order to evaluate the optimization algorithm, the water boiler is used as the controllable load. Two case studies for 24-hour illustrate that the implementation controlled by HEMS using this algorithm can contribute greatly to the energy bill savings. According to the two implementations, around 43% of reduction for the energy bill can be achieved. Considering the PV production integrated into HEMS and support from the distribution network operators, the more benefits can be achieved. The switch panel in the AC Microgrid laboratory acts a crucial part in the implementations and it has seven 3-phase channels that can be utilized to connect with electrical appliances, home energy storage systems, and distributed generations such as micro wind turbines and roof-mounted PV panels. Each channel is equipped with two connectors to control the operation, the top of which is controlled by HEMS computer using wireless Z-Wave and the other one is controlled by dSpace which can be used to emulate the consumption pattern in the households. The algorithm and Z-wave interface are implemented in Matlab / Simulink environment and Z-wave makes it possible to control the boiler or other loads in real-time

Análise de dados para previsão de micro produção de energia solar e eólica

A liberalização do mercado energético, combinado com a utilização de energias renováveis, tem provocado uma grande transformação no sistema elétrico de energia, tornando-se numa rede inteligente, distribuída e sustentável, designada por “smart grid". Neste novo paradigma, a rede de energia elétrica deixa de estar assente numa estrutura centralizada e passa a ser assente numa estrutura descentralizada, compreendendo micro produção distribuída, assente em fontes renováveis de energia, como são a produção solar fotovoltaica e eólica. Neste novo paradigma energético, as tecnologias de informação e comunicação assumem particular relevância na gestão inteligente e eficiente dos recursos e infraestruturas, e ainda integrar os consumidores como participantes ativos na gestão do sistema elétrico de energia (incluindo também os veículos elétricos e a produção usando energias renováveis). O conhecimento do comportamento da produção solar fotovoltaica e eólica para horizontes temporais relativamente curtos tem grande importância nesta gestão inteligente, conduzindo a ganhos económicos. Neste trabalho foi realizada a análise dos dados de produção da micro rede do IPB, proveniente de sistemas solar fotovoltaicos, correlacionando esses dados com dados meteorológicos de forma a criar modelos de previsão da produção mais fiáveis. Estes modelos de previsão da energia produzida permitem a monitorização em tempo real dos equipamentos de produção, permitindo a deteção de avarias ou degradação na sua operação, assim como uma previsão a produção de energia elétrica a curto e médio prazo, permitindo balancear mais eficazmente o fluxo de energia da micro rede de energia elétrica em análise. Para o efeito foi utilizada a plataforma WEKA e vários algoritmos de análise de dados, nomeadamente, redes neurais artificiais, árvores de decisão, regras e regressão linear. No qual foram obtidos bons modelos de previsão quer para previsão a curto e a médio prazo.The liberalization of the energy market, combined with the use of renewable energy, has caused a major transformation in the electric power system, making it a smart grid, distributed and sustainable. In this new paradigm, the power grid is no longer based on a centralized structure and is now based on a decentralized structure, comprising micro distributed generation, based on renewable energy sources, as are solar photovoltaic and wind production. In this new energy paradigm, the information and communication technologies are particularly relevant in the intelligent and efficient management of resources and infrastructure, and also integrate consumers as active participants in the electrical system power management (also including electric vehicles and production using energy renewable). Knowledge of the behaviour of the solar photovoltaic and wind production for relatively short time frames is of great importance in this intelligent management, leading to economic gains. This work was carried out micro analysis of production data network IPB from solar photovoltaic systems, by correlating these data with weather data so as to create predictive models of the most reliable output. These models forecast the energy produced allow real-time monitoring of production equipment, enabling the detection of faults or degradation in its operation, as well as a forecast production of electricity in the short and medium term, allowing balance more effectively the flow energy micro power grid under review. For this purpose it used WEKA platform and various data analysis algorithms, namely, artificial neural networks, decision trees, rules, and linear regression. In which it was obtained good predictive models both for the short and medium-term forecast