Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Household Power Consumption Forecasting using IoT Smart Home Data

The use of the forecasting system is becoming more prominent in recent years. One of the implementations of the forecasting system is to predict electricity consumption demand. In this paper, we have developed a forecasting system for household electricity consumption using a well-known Extreme Gradient Boosting algorithm. We utilized time-series data from a smart meter dataset to make a predictive model. First, we evaluated the importance of time-series feature from the dataset and resampled the original dataset. Then, we used the resampled data to train the model and calculated training loss function. Our experimental studies with real IoT Smart Home data demonstrate that our forecasting system works well with small dataset using one-hour downsampling on the dataset

The International Demand Management Framework Stage 1

This report forms part of a larger study (Stage 1 of the International Demand Management Framework (IDMF)) which has been undertaken under the auspices of the International Water Association Task Force 7 of the Specialist Group Efficient Operation and Management. Current practice often utilises litres per capita per day (LCD) to describe and forecast water demand; however this practice has been found to be limited for planning purposes within water utilities. In its place, an emerging way forward is based on disaggregation of demand and robust comparison of both demand and supply options to improve reliability. Disaggregation of demand into sectors and end uses allows accurate forecasting of demand and strategic design of demand management options which may be used in complement to supply options. The findings indicate that Canal de Isabel II has completed excellent work in certain areas, such as drought and risk management, management of water losses, knowledge of supply and distribution system, and sector and end use data collection. There remains significant opportunity for Canal de Isabel II to incorporate other improvements toward best practice, including the following: ·approach the planning process in a coherent way that considers both demand and supply options and works through a logical sequence of steps ·utilise in-depth knowledge of sector and end-uses to strategically identify and design demand management options ·compare demand and supply options using a consistent economic analysis so that the solutions with the lowest cost to society can be selected and implemented ·involve a larger group of stakeholders at appropriate points in the planning process ·conduct pilot and implementation of chosen demand management options to initiate on-going learning about what works and doesn't in the local context & ·monitor and evaluate pilot and implementation programs using robust statistical methods

Short-Term Load Forecasting Using AMI Data

Accurate short-term load forecasting is essential for efficient operation of the power sector. Predicting load at a fine granularity such as individual households or buildings is challenging due to higher volatility and uncertainty in the load. In aggregate loads such as at grids level, the inherent stochasticity and fluctuations are averaged-out, the problem becomes substantially easier. We propose an approach for short-term load forecasting at individual consumers (households) level, called Forecasting using Matrix Factorization (FMF). FMF does not use any consumers' demographic or activity patterns information. Therefore, it can be applied to any locality with the readily available smart meters and weather data. We perform extensive experiments on three benchmark datasets and demonstrate that FMF significantly outperforms the computationally expensive state-of-the-art methods for this problem. We achieve up to 26.5% and 24.4 % improvement in RMSE over Regression Tree and Support Vector Machine, respectively and up to 36% and 73.2% improvement in MAPE over Random Forest and Long Short-Term Memory neural network, respectively

Statistical Models of Domestic And SME Daily Gas Consumption - Applications To Gas Network Planning And Management

This research is centred on three pillars of EU energy policy that aim to improve: 1) energy efficiency, in order to reduce CO2 emissions and therefore limit climate change; 2) security of energy supplies, in order to protect economic output and vulnerable citizens in extreme weather; and 3) market integration, in order to increase energy supplier competition and consumer choice in each member state. To help deliver on these policies, the EU has recently mandated that: 1) gas smartmeters are to be provided to consumers to help improve energy efficiency; 2) network operators ensure adequate gas supplies during extreme cold weather; and 3) network operators provide energy suppliers with forecasts of the volume of gas they should purchase each day in wholesale markets in order to limit the risk to suppliers when entering new markets. Gas Networks Ireland has part-funded this research and has provided smart-metering and network gas consumption data, so that bottom-up and top-down models of gas consumption can be developed to assist with these EU requirements. Bottom-up models can be used to assess building energy efficiency and to forecast the daily volume of gas to be purchased by an energy supplier for its consumer portfolio. Top-down models can be used to forecast peak-day consumption on the network during extreme weather, and to improve the accuracy of bottom-up portfolio forecasts. This research develops such models using both ordinary and non-linear least squares (OLS and NLS) regression modelling methods. Each of the resulting models is either based on or develops upon standard heating degree day (HDD) theory used to model iii building heating system fuel consumption. It is shown that HDDs are used as an explanatory variable in linear regression models of building gas consumption and that these models can be used to infer building energy performance. This is used as a basis on which to develop a new energy efficiency benchmarking tool for domestic dwellings. This tool is for the use of energy suppliers who must assist their consumers in making energy savings. It is also shown that the HDD approach can be extended to include other variables such as wind speed and solar radiation. This is used as a basis to develop adapted HDD variables to improve estimates of daily gas consumption of individual buildings and of the Irish domestic and SME gas market. These variables are used to develop improved models for bottom-up portfolio and peak-day network forecasting. The development of the new benchmarking tool is based on the availability of gas smart-metering and household survey data for a sample of dwellings. It is shown that these data allow each parameter of a HDD linear regression model to be estimated using non-linear regression methods rather than the traditional ‘trial and error’ methods applied to monthly or longer fuel consumption data. This improved method is used to estimate HDD models for the dwelling sample and the resulting distribution of independent parameters are presented. These parameter distributions are then characterised by multinomial logistic regression (MLR) models using descriptive household variables. These MLR models are then used to demonstrate a new energy efficiency benchmarking method by comparing the inferred energy end-use of similar buildings. The NLS regression modelling method is also used to develop an adapted HDD variable to improve estimates of total daily domestic and SME gas market consumption. The resulting model is based on the availability of recent market consumption data and accounts for numerous effects on gas consumption in addition to those currently estimated by the HDD variable. The improvement in modelling accuracy is quantified by applying a comparative analysis for each of the additional effects accounted for by the new adapted HDD variable. It is found that solar radiation significantly affects gas consumption and should be considered in market consumption models. The new model is used to predict year-ahead peak-day market consumption to alternative supply standards. Finally, the research develops new models of daily gas consumption for individual consumers based on smart-metering data. These models are developed using SME smart-metering data. This is challenging because their consumption is unpredictable relative to domestic consumers, leading to forecasting difficulties for network operators and energy suppliers. Two modelling options are investigated: one that applies an adapted HDD variable (similar to that referred to above) to estimate the daily gas consumption of individual enterprises using the NLS method; and a second that applies the same market consumption estimator to each enterprises using the OLS method. It is found that OLS models are the most suitable for individual consumer forecasting in terms of the practicality of their implementation and accuracy of their forecasts

Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting

Energy efficiency and consumers' role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers' power converters track the power setpoints obtained from the proposed strategy.Spanish Agencia Estatal de Investigacion ; Fondo Europeo de Desarrollo Regional

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science