Intelligent techniques for forecasting electricity consumption of buildings

Amber, Khuram Pervez; Ahmad, Rizwan; Aslam, Muhammad Waqar; Kousar, Anila; Usman, Muhammad; Khan, Muhammad Sajid

doi:10.1016/j.energy.2018.05.155

Cited by 163 publications

(63 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While energy consumption is obviously aggregated across many individual dwellings at the building level which lessens its inherent volatility, forecasting nonetheless remains a challenging task due to the wide-ranging factors which affect consumption such as the weather, equipment, number of occupants and their energy-use behavior [3]. Notwithstanding the task complexity, prediction models which achieve even a small percentage of improvement can reap large economic and social benefits when applied to high levels of energy consumption in large buildings, wherein lies the attraction of forecasting building energy consumption as a topic for research [10].…”

Section: Building Energy Consumption Predictionmentioning

confidence: 99%

“…Amber et al [10] reported the use of multi-layer feedforward (MLFF) neural network and deep learning for predicting the daily electrical consumption of an administrative building in London. The building data used comprised daily electricity usage, daily mean surrounding temperature, daily mean global irradiance, daily mean humidity, daily mean wind velocity, and weekday index.…”

Section: Building Energy Consumption Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

et al. 2019

View full text Add to dashboard Cite

With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.

show abstract

Section: Building Energy Consumption Predictionmentioning

confidence: 99%

Section: Building Energy Consumption Predictionmentioning

confidence: 99%

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Within the scientific article [16], Amber et al made a comparative study of five different prediction techniques for the daily electricity consumption in the case of an administrative building situated in the capital of the United Kingdom. The data used in the experiments consisted in variables like temperature, solar radiation, humidity, wind speed, and weekday index.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For each of the three above-mentioned training algorithms (LM, BR and SCG), 16 ANNs have been developed in order to forecast the month-ahead daily consumed electricity, having various architectures, namely, four neurons for the input data (one neuron for the daily electricity consumption dataset, three neurons for the timestamps exogenous data), a hidden layer's size of n neurons, where n ∈ {8, 16, 24, 48}, the delay parameter taking the values d ∈ {7, 14, 21, 28}, the output layer containing one neuron and also one neuron for the output data (measured in MW h).…”

Section: Stage Ii: Developing the Narx Ann Forecasting Solution For Tmentioning

confidence: 99%

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Petroşanu¹

2019

Processes

View full text Add to dashboard Cite

An accurate forecast of the electricity consumption is particularly important to both consumers and system operators. The purpose of this study is to develop a forecasting method that provides such an accurate forecast of the month-ahead hourly electricity consumption in the case of medium industrial consumers, therefore assuring an intelligent energy management and an efficient economic scheduling of their resources, having the possibility to negotiate in advance appropriate billing tariffs relying on accurate hourly forecasts, in the same time facilitating an optimal energy management for the dispatch operator. The forecasting method consists of developing first non-linear autoregressive, with exogenous inputs (NARX) artificial neural networks (ANNs) in order to forecast an initial daily electricity consumption, a forecast that is being further processed with custom developed long short-term memory (LSTM) neural networks with exogenous variables support in order to refine the daily forecast as to achieve an accurate hourly forecasted consumed electricity for the whole month-ahead. The obtained experimental results (highlighted also through a very good value of 0.0244 for the root mean square error performance metric, obtained when forecasting the month-ahead hourly electricity consumption and comparing it with the real consumption), the validation of the developed forecasting method, the comparison of the method with other forecasting approaches from the scientific literature substantiate the fact that the proposed approach manages to fill a gap in the current body of knowledge consisting of the need of a high-accuracy forecasting method for the month-ahead hourly electricity consumption in the case of medium industrial consumers. The developed forecasting method targets medium industrial consumers, but, due to its accuracy, it can also be a useful tool for promoting innovative business models with regard to industrial consumers willing to produce a part of their own electricity using renewable energy resources, benefiting from reduced production costs and reliable electricity prices.

show abstract

“…AI techniques have received increasing attention as a powerful computational tool for STEF forecasting since 1980. These techniques cover artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), fuzzy systems (FS), evolutionary computation, and swarm intelligence [33]. AI techniques are able to solve nonlinear problems, and complex relationships, and can be used for adaptive control and decision making under uncertainty [32].…”

Section: Short Term Energy Forecasting For the Mg's Load And Generatimentioning

confidence: 99%

Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

2019

View full text Add to dashboard Cite

A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment.Energies 2019, 12, 2712 2 of 26 these ESS into small-scale MG architectures. The electrical load profiles of small scale MGs, particularly residential communities, can vary considerably with time: periods of house inoccupancy (e.g., during vacation) and the addition of new equipment (e.g., electric vehicle charge) or even new houses can have a strong influence on the loading profile [9]. Additionally, these small-scale systems see sharp changes in load over a short period of time as single loads (shower, cooker) can be significant considering the size of the MG. For these reasons, EMS used for small scale MGs must have a short control sample time to observe and respond to fast changes in the load and generation throughout the day [9,10]. Also, energy forecasting techniques must be adaptive and also have a short sample time if they are to help the EMS achieve good results for this type of grid.Small-scale MGs should preferably operate as a single controllable unit that imports/exports power from/to the main grid following a predictable shape [11]. In this way, the energy community works for the benefit of the whole grid and not just the small scale MG [12]. To achieve this, a real-time controller is required that allows the small-scale MG to accurately follow a reference value for the power drawn from the main electric grid, where this reference is created by a higher level controller which considers both local and system wide factors.Alternatively, large scale energy storage systems (ESS) (>1 MWh) will play a key role in solving problems such as intermittency of supply and loss of inertia which are challenging electricity grid operation [13], and many grid operators are encouraging the use of ESS to address, for example, increasing demand peaks and network congestion [14].Much of the existing research focusing on microgrid energy management (MGEM) is oriented towards determining the best operating scenario for the MG [15][16][17]. In [18], Carlos et al. introduce a new iterative algorithm that mana...

show abstract

Intelligent techniques for forecasting electricity consumption of buildings

Cited by 163 publications

References 46 publications

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

Contact Info

Product

Resources

About