Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior

Zipperer, Adam; Aloise-Young, Pat; Suryanarayanan, Siddharth; Zimmerle, Daniel; Roche, Robin; Earle, L.; Christensen, Dane; Bauleo, P.

doi:10.2172/1220236

Cited by 47 publications

(39 citation statements)

References 1 publication

(1 reference statement)

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“…• the model has been trained according to 20 training periods with a dataset size of 60 samples • for the evaluation of the error is performed by the "Mean Squarred Error" and the gradient with the "Adam" optimizer Below is listed the code executing LSTM algorithm: model = Sequential() model.add(LSTM(100, input_shape=(train_X.shape [1], train_X.shape [2]))) model.add (Dropout(0.2)) model.add (Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') # fit network history = model. fit(train_X, train_y, epochs=20, batch_size=60, validation_data=(test_X, test_y) In Fig.…”

Section: Lstm Neural Network Predictive Resultsmentioning

confidence: 99%

“…In [1], some authors highlighted the importance of Machine Learning (ML) algorithms for the planning of the activation of electric utilities starting from the distribution of electrical energy and by analyzing predictive data [1]. Other researchers studied accurately the topic of electric energy management in the smart home, by analyzing the combined architecture of the electric power transmission together with the communication network using smart meters [2]. The use of the smart meters is useful for the real time reading of the electrical power providing, though an external control unit, information about the "status" of the power consumption and reconstruction of periodic load curve.…”

Section: Introduction: State Of the Art And Main Project Specificatmentioning

confidence: 99%

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2018

IJSCAI

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In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization.

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Section: Lstm Neural Network Predictive Resultsmentioning

confidence: 99%

Section: Introduction: State Of the Art And Main Project Specificatmentioning

confidence: 99%

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2018

IJSCAI

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“…Consumer behavior is dependent on weather and seasons and has a variable influence on energy consumption decisions. Thereby, actively engaging consumers in personalized energy management by facilitating well-timed feedback on energy consumption and related cost is key to steering suitable energy saving schemes or programs [3]. Therefore, designing models that are capable of analyzing energy time series from smart meters that is capable of intelligently infer and forecast the energy usage is very critical.…”

Section: Introductionmentioning

confidence: 99%

“…For examples, extensive arguments in support of exploiting behavioral energy consumption information to encourage and obtain greater energy efficiency are made in [2,3]. The impact of behavioral changes for energy savings was also examined by [4,5] and end-user participation towards effective and improved energy savings were emphasized.…”

Section: Introductionmentioning

confidence: 99%

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

2018

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Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers' energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.

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“…The increasing deployment of smart meters to people's homes results in abundant quantities of data that need to be processed by power utilities [25]. Cloud computing platforms can bring great scalability and availability concerning network computational resources, bandwidth, and storage.…”

Section: Introductionmentioning

confidence: 99%

Experimental Results on a Wireless Wattmeter Device for the Integration in Home Energy Management Systems

et al. 2017

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This paper presents a home area network (HAN)-based domestic load energy consumption monitoring prototype device as part of an advanced metering system (AMS). This device can be placed on individual loads or configured to measure several loads as a whole. The wireless communication infrastructure is supported on IEEE 805.12.04 radios that run a ZigBee stack. Data acquisition concerning load energy transit is processed in real time and the main electrical parameters are then transmitted through a RF link to a wireless terminal unit, which works as a data logger and as a human-machine interface. Voltage and current sensing are implemented using Hall effect principle-based transducers, while C code is developed on two 16/32-bit microcontroller units (MCUs). The main features and design options are then thoroughly discussed. The main contribution of this paper is that the proposed metering system measures the reactive energy component through the Hilbert transform for low cost measuring device systems.

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Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior

Cited by 47 publications

References 1 publication

Untitled

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Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Experimental Results on a Wireless Wattmeter Device for the Integration in Home Energy Management Systems

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