Predicting the Energy Consumption of Residential Buildings for Regional Electricity Supply-Side and Demand-Side Management

Cai, Huiling; Shen, Shoupeng; Lin, Qingcheng; Li, Xuefeng; Xiao, Hui

doi:10.1109/access.2019.2901257

Cited by 95 publications

(35 citation statements)

References 37 publications

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“…In order to set the input parameters of DR intelligently and efficiently, we need to have an estimation of the load behavior. This implies the necessity of predicting the load consumption during time to support adjusting incentives or tariffs [20,21].…”

Section: Demand Responsementioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

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The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

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Section: Demand Responsementioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

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“…In the second stage, a global power consumption profile was derived from the local ones. Furthermore, Cai et al [14] applied an improved k-means algorithm with particle swarm optimization (PSO) to open residential buildings dataset to divide their electricity consumption in an entire region into different levels. To extract the daily electricity consumption behavior of a household, Nordahl et al [15] used the centroids of the generated clusters by k-medoids.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

Ullah

Haydarov

Haq

et al. 2020

Sensors

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The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers’ electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.

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“…Thus, researchers gradually turned their attention to SVRs. Cai et al [6] proved the superiority of an SVM in predicting building energy consumption. Zhang et al [7] applied an SVR to accomplish the nonlinear regression prediction of building energy consumption.…”

Section: Introductionmentioning

confidence: 99%

District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

et al. 2019

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The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

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Predicting the Energy Consumption of Residential Buildings for Regional Electricity Supply-Side and Demand-Side Management

Cited by 95 publications

References 37 publications

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

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