The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible loads and energy storage. The ability to forecast consumption at different locations in distribution systems will be a key capability of Smart Grids. The goal of this paper is to benchmark state-of-the-art methods for forecasting electricity demand on the household level across different granularities and time scales in an explorative way, thereby revealing potential shortcomings and find promising directions for future research in this area. We apply a number of forecasting methods including ARIMA, neural networks, and exponential smoothening using several strategies for training data selection, in particular day type and sliding window based strategies. We consider forecasting horizons ranging between 15 minutes and 24 hours. Our evaluation is based on two data sets containing the power usage of individual appliances at second time granularity collected over the course of several months. The results indicate that forecasting accuracy varies significantly depending on the choice of forecasting methods strategy and the parameter configuration. Measured by the Mean Absolute Percentage Error (MAPE), the considered state-of-the-art forecasting methods rarely beat corresponding persistence forecasts. Overall, we observed MAPEs in the range between 5 and >100 . The average MAPE for the first data set was 30 , while it was 85 for the other data set. These results show big room for improvement. Based on the identified trends and experiences from our experiments, we contribute a detailed discussion of promising future research.
First studies have analyzed the potential and first prototypes of consumption forecasts for individual households (e.g. @cite_25 , @cite_26 ). However, most work on household consumption focuses on disaggregation of electricity consumption. Examples include @cite_23 , @cite_24 , @cite_2 , @cite_9 and @cite_29 . The authors of @cite_22 give an overview of the state-of-the-art in this area. In this paper, we benchmark state-of-the-art forecasting models for household consumption and also evaluate how the disaggregation of consumption data influences the prediction of household consumption.
@cite_26 @cite_22 @cite_9 @cite_29 @cite_24 @cite_23 @cite_2 @cite_25
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This paper presents a model to simulate the electricity demand of a single household consisting of multiple individuals. The total consumption is divided into four main categories, namely cold appliances, heating, ventilation, and air conditioning, lighting, and energy consumed by household members’ activities. The first three components are modeled using engineering physically-based models, while the activity patterns of individuals are modeled using a heterogeneous Markov chain. Using data collected by the U.S. Bureau of Labor Statistics, a case study for an average American household is developed. The data are used to conduct an in-sample validation of the modeled activities and a rigorous statistical validation of the predicted electricity demand against metered data is provided. The results show highly realistic patterns that capture annual and diurnal variations, load fluctuations, and diversity between household configuration, location, and size. The aim of this paper is to quantify the impact of disaggregated electric power measurements on the accuracy of household demand forecasts. Demand forecasting on the household level is regarded as an essential mechanism for matching distributed power generation and demand in smart power grids. We use state-of-the-art forecasting tools, in particular support vector machines and neural networks, to evaluate the use of disaggregated smart home sensor data for household-level demand forecasting. Our investigation leverages high resolution data from 3 private households collected over 30 days. Our key results are as follows: First, by comparing the accuracy of the machine learning based forecasts with a persistence forecast we show that advanced forecasting methods already yield better forecasts, even when carried out on aggregated household consumption data that could be obtained from smart meters (1-7 ). Second, our comparison of forecasts based on disaggregated data from smart home sensors with the persistence and smart meter benchmarks reveals further forecast improvements (4-33 ). Third, our sensitivity analysis with respect to the time resolution of data shows that more data only improves forecasting accuracy up to a certain point. Thus, having more sensors appears to be more valuable than increasing the time resolution of measurements. The recent development of smart meters has allowed the analysis of household electricity consumption in real time. Predicting electricity consumption at such very low scales should help to increase the efficiency of distribution networks and energy pricing. However, this is by no means a trivial task since household-level consumption is much more irregular than at the transmission or distribution levels. In this work, we address the problem of improving consumption forecasting by using the statistical relations between consumption series. This is done both at the household and district scales (hundreds of houses), using various machine learning techniques, such as support vector machine for regression (SVR) and multilayer perceptron (MLP). First, we determine which algorithm is best adapted to each scale, then, we try to find leaders among the time series, to help short-term forecasting. We also improve the forecasting for district consumption by clustering houses according to their consumption profiles. Source separation of whole-home electrical consumption also known as energy disaggregation plays a crucial role in energy savings and sustainable development. One important approach towards accurate energy disaggregation is based on sparse code learning. The sparsity-based source separation algorithms allow to build models that explicitly generalize across multiple different devices of the same category. While this method has recently been investigated, yet the importance of the degree of sparseness given by the regularization parameter is rarely considered. In this paper we aim at investigating the performance of learning representations from the aggregated electrical load signal with sparse models for energy disaggregation. In particular we focus our study on the influence of the regularization parameter in the overall approach. The computational experiments yielded in real data from home electrical energy consumption show that for several degrees of sparseness a reliable scheme for energy disaggregation can be obtained with statistical significance. In contemporary power systems, residential consumers may account for up to 50 of a country's total electrical energy consumption. Even though they constitute a significant portion of the energy market, not much has been achieved towards eliminating the inability for energy suppliers to perform long-term portfolio management, thus maximizing their revenue. The root cause of these problems is the difficulty in modeling consumers' behavior, based on their everyday activities and personal comfort. If one were able to provide targeted incentives based on consumer profiles, the expected impact and market benefits would be significant. This paper introduces a formal residential consumer modeling methodology, that allows (i) the decomposition of the observed electrical load curves into consumer activities and, (ii) the evaluation of the impact of behavioral changes on the household's aggregate load curve. Analyzing electrical consumption measurements from DEHEMS research project enabled the model extraction of real-life consumers. Experiments indicate that the proposed methodology produces accurate small-scale consumer models and verify that small shifts in appliance usage times are sufficient to achieve significant peak power reduction. A model of electric residential end-use is proposed for establishing the load diagram of an area by a process of synthesis. The model follows a "bottom-up" approach, allowing construction of the relative load shape of the area, starting from knowledge of its most relevant socioeconomic and demographic characteristics, unitary energy consumption and the load profiles of individual household appliances. Several probability functions have been introduced in order to cover the close relationship existing between the demand of residential customers and the psychological and behavioral factors typical of the household; the model makes frequent use of the latter through a Monte Carlo extraction process. The model has been applied for the simulation of a residential area where field measurements of power demand had been made at 15-minute intervals and a combined mail survey had been conducted to investigate household energy usage. The paper reports the results of a comparison between recorded and predicted load profiles. > Correctly anticipating load characteristics of low voltage level is getting increased interest by distribution network operators. Energy disaggregation could be one of the potential approaches to exploit the massive amount of smart meter data to fulfill the task. Proper individual home appliance modeling is critical to the performance of NILM. In this paper, a hierarchical hidden Markov model (HHMM) framework to model home appliances is proposed. This model aims to provide better representation for those appliances that have multiple built-in modes with distinct power consumption profiles, such as washing machines and dishwashers. The dynamic Bayesian network representation of such an appliance model is built. A forward–backward algorithm, which is based on the framework of expectation maximization, is formalized for the HHMM fitting process. Tests on publically available data show that the HHMM and proposed algorithm can effectively handle the modeling of appliances with multiple functional modes, as well as better representing a general type of appliances. A disaggregation test also demonstrates that the fitted HHMM can be easily applied to a general inference solver to outperform conventional hidden Markov model in the estimation of energy disaggregation. Residential energy constitutes a significant portion of the total US energy consumption. Several researchers proposed energy-aware solutions for houses, promising significant energy and cost savings. However, it is important to evaluate the outcomes of these methods on larger scale, with hundreds of houses. This paper presents a human-activity based residential energy modeling framework, that can create power demand profiles considering the characteristics of household members. It constructs a mathematical model to show the detailed relationships between human activities and house power consumption. It can be used to create various house profiles with different energy demand characteristics in a reproducible manner. Comparison with real data shows that our model captures the power demand differences between different family types and accurately follows the trends seen in real data. We also show a case study that evaluates voltage deviation in a neighborhood, which requires accurate estimation of the trends in power consumption. Realistic time-resolved data on occupant behaviour, presence and energy use are important inputs to various types of simulations, including performance of small-scale energy systems and buildings' indoor climate, use of lighting and energy demand. This paper presents a modelling framework for stochastic generation of high-resolution series of such data. The model generates both synthetic activity sequences of individual household members, including occupancy states, and domestic electricity demand based on these patterns. The activity-generating model, based on non-homogeneous Markov chains that are tuned to an extensive empirical time-use data set, creates a realistic spread of activities over time, down to a 1-min resolution. A detailed validation against measurements shows that modelled power demand data for individual households as well as aggregate demand for an arbitrary number of households are highly realistic in terms of end-use composition, annual and diurnal variations, diversity between households, short time-scale fluctuations and load coincidence. An important aim with the model development has been to maintain a sound balance between complexity and output quality. Although the model yields a high-quality output, the proposed model structure is uncomplicated in comparison to other available domestic load models. The increasing use of renewable energy is leading to a paradigm shift in operating electrical grids. Production is moving away from centralized power plants to decentralized sources like solar panels and windmills. One consequence of this development is the need for managing supply and demand in local distribution grids in a "smart" way, which also implies the capability to forecast the demand for electric power closer to the end consumer and on shorter time scales than today. In this paper, we describe a system prototype for electricity demand forecasting based on highly disaggregated data from sensors deployed in homes and evaluate its performance both with respect to forecasting accuracy and ICT resource requirements. The data we use for our evaluation was collected in a pilot trial. Our system prototype combines complex event processing with state-of-the-art forecasting capabilities. For short-term forecasts, we observed average error reductions of up to 98 percentage points compared to average demand profiles. Our experiments also show the applicability of our approach at large scale. We were able to run the forecasting service for 1,000 households in parallel on one off-the-shelf server. In this paper we present a data-driven approach to modeling end user energy consumption in residential and commercial buildings. Our model is based upon a data set of monthly electricity and gas bills, collected by a utility over the course of several years, for approximately 6,500 buildings in Cambridge, MA. In addition, we use publicly available tax assessor records and geographical survey information to determine corresponding features for the buildings. Using both parametric and non-parametric learning methods, we learn models that predict distributions over energy usage based upon these features, and use these models to develop two end-user systems. For utilities or authorized institutions (those who may obtain access to the full data) we provide a system that visualizes energy consumption for each building in the city; this allows companies to quickly identify outliers (buildings which use much more energy than expected even after conditioning on the relevant predictors), for instance allowing them to target homes for potential retrofits or tiered pricing schemes. For other end users, we provide an interface for entering their own electricity and gas usage, along with basic information about their home, to determine how their consumption compares to that of similar buildings as predicted by our model. Merely allowing users to contextualize their consumption in this way, relating it to the consumption in similar buildings, can itself produce behavior changes to significantly reduce consumption. Abstract Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted. We consider the problem of forecasting the aggregate demand of a pool of price-responsive consumers of electricity. The response of the aggregate load to price is modeled by an optimization problem that is characterized by a set of marginal utility curves and minimum and maximum power consumption limits. The task of estimating these parameters is addressed using a generalized inverse optimization scheme that, in turn, requires solving a nonconvex mathematical program. We introduce a solution method that overcomes the nonconvexities by solving instead two linear problems with a penalty term, which is statistically adjusted by using a cross-validation algorithm. The proposed methodology is data-driven and leverages information from regressors, such as time and weather variables, to account for changes in the parameter estimates. The power load of a group of heating, ventilation, and air conditioning systems in buildings is simulated, and the results show that the aggregate demand of the group can be successfully captured by the proposed model, making it suitable for short-term forecasting purposes. Abstract User behaviour plays a key role in the energy demand of residential buildings, and its importance will only increase when moving towards nearly Zero-Energy homes. However, little detailed information is available on how users interact with their homes. Due to the lack of information, user behaviour is often included in building performance simulations through one standard user pattern. To obtain more accurate energy demand simulations, user patterns are needed that capture the wide variations in behaviour without making simulations overly complicated. To this end, we developed a probabilistic model which generates realistic occupancy sequences that include three possible states: (1) at home and awake, (2) sleeping or (3) absent. This paper reports on the methodology used to construct this occupancy model based on the 2005 Belgian time-use survey. Using hierarchical clustering, we were able to identify seven typical occupancy patterns. The modelling of individual occupancy sequences based on this method enables to include highly differentiated yet realistic behaviour that is relevant to building simulations and can be used for individualised feedback based on peer comparison. The model's calibration data is available for download [1] . There is a growing interest in reducing energy consumption and the associated greenhouse gas emissions in every sector of the economy. The residential sector is a substantial consumer of energy in every country, and therefore a focus for energy consumption efforts. Since the energy consumption characteristics of the residential sector are complex and inter-related, comprehensive models are needed to assess the technoeconomic impacts of adopting energy efficiency and renewable energy technologies suitable for residential applications. The aim of this paper is to provide an up-to-date review of the various modeling techniques used for modeling residential sector energy consumption. Two distinct approaches are identified: top-down and bottom-up. The top-down approach treats the residential sector as an energy sink and is not concerned with individual end-uses. It utilizes historic aggregate energy values and regresses the energy consumption of the housing stock as a function of top-level variables such as macroeconomic indicators (e.g. gross domestic product, unemployment, and inflation), energy price, and general climate. The bottom-up approach extrapolates the estimated energy consumption of a representative set of individual houses to regional and national levels, and consists of two distinct methodologies: the statistical method and the engineering method. Each technique relies on different levels of input information, different calculation or simulation techniques, and provides results with different applicability. A critical review of each technique, focusing on the strengths, shortcomings and purposes, is provided along with a review of models reported in the literature. With recent developments in the infrastructure of smart meters and smart grid, more electric power data is available and allows real-time easy data access. Modeling individual home appliance loads is important for tasks such as non-intrusive load disaggregation, load forecasting, and demand response support. Previous methods usually require sub-metering individual appliances in a home separately to determine the appliance models, which may not be practical, since we may only be able to observe aggregated real power signals for the entire-home through smart meters deployed in the field. In this paper, we propose a model, named Explicit-Duration Hidden Markov Model with differential observations (EDHMM-diff), for detecting and estimating individual home appliance loads from aggregated power signals collected by ordinary smart meters. Experiments on synthetic data and real data demonstrate that the EDHMM-diff model and the specialized forward-backward algorithm can effectively model major home appliance loads. Energy disaggregation is the task of taking a whole-home energy signal and separating it into its component appliances. Studies have shown that having device-level energy information can cause users to conserve significant amounts of energy, but current electricity meters only report whole-home data. Thus, developing algorithmic methods for disaggregation presents a key technical challenge in the effort to maximize energy conservation. In this paper, we examine a large scale energy disaggregation task, and apply a novel extension of sparse coding to this problem. In particular, we develop a method, based upon structured prediction, for discriminatively training sparse coding algorithms specifically to maximize disaggregation performance. We show that this significantly improves the performance of sparse coding algorithms on the energy task and illustrate how these disaggregation results can provide useful information about energy usage. Abstract Forecasting of energy demand in emerging markets is one of the most important policy tools used by the decision makers all over the world. In Turkey, most of the early studies used include various forms of econometric modeling. However, since the estimated economic and demographic parameters usually deviate from the realizations, time-series forecasting appears to give better results. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) methods to estimate the future primary energy demand of Turkey from 2005 to 2020. The ARIMA forecasting of the total primary energy demand appears to be more reliable than the summation of the individual forecasts. The results have shown that the average annual growth rates of individual energy sources and total primary energy will decrease in all cases except wood and animal–plant remains which will have negative growth rates. The decrease in the rate of energy demand may be interpreted that the energy intensity peak will be achieved in the coming decades. Another interpretation is that any decrease in energy demand will slow down the economic growth during the forecasted period. Rates of changes and reserves in the fossil fuels indicate that inter-fuel substitution should be made leading to a best mix of the country's energy system. Based on our findings we proposed some policy recommendations. Flattening household electricity demand reduces generation costs, since costs are disproportionately affected by peak demands. While the vast majority of household electrical loads are interactive and have little scheduling flexibility (TVs, microwaves, etc.), a substantial fraction of home energy use derives from background loads with some, albeit limited, flexibility. Examples of such devices include A Cs, refrigerators, and dehumidifiers. In this paper, we study the extent to which a home is able to transparently flatten its electricity demand by scheduling only background loads with such flexibility. We propose a Least Slack First (LSF) scheduling algorithm for household loads, inspired by the well-known Earliest Deadline First algorithm. We then integrate the algorithm into Smart-Cap, a system we have built for monitoring and controlling electric loads in homes. To evaluate LSF, we collected power data at outlets, panels, and switches from a real home for 82 days. We use this data to drive simulations, as well as experiment with a real testbed implementation that uses similar background loads as our home. Our results indicate that LSF is most useful during peak usage periods that exhibit “peaky” behavior, where power deviates frequently and significantly from the average. For example, LSF decreases the average deviation from the mean power by over 20 across all 4-hour periods where the deviation is at least 400 watts. Abstract This paper presents foresee™, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users’ needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliance models and usage patterns to predict the home’s future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6 whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10 or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. These benefits are provided while maintaining the occupants’ thermal comfort or convenience in using their appliances.