This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work ...
In [27], a DQN-based optimal management strategy was proposed for low-carbon HEMS to minimize user dissatisfaction, carbon trading, and energy consumption. This strategy outperformed day-ahead forecasting-based management and demonstrated good performance in stochastic environments with high stability and...
(2) Hierarchical forecasting. Different zones have differ- ent electricity consumption behaviors. For instance, Zone 9 represents an industrial customer load, which is largely not weather sensitive. In order to utilize the hierarchical information fully, the participants may choose a bottom-up, middle...
So, UC-CEED is a combinatorial, dual-objective nonlinear optimization problem that requires efforts to optimally commit and schedule conventional thermal units by minimizing fuel consumption and emissions to meet load demand and losses while adhering to all system constraints. This complex constrained ...
energy consumption. This metric is calculated by taking a cleaned version of the data set in which days with only zero readings are removed and finding the Spearman rank-order correlation coefficient between the meter reading and the outside air temperature across each month. The resultant heat ma...
Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on
With Root Mean Square Error of 5.7532Wh, Mean Absolute Percentage Error of 3.5001%, Mean Absolute Error of 6.7532Wh andR2of 0.9701, the hybrid model fared better than other models on the 'Electric Power Consumption' Kaggle dataset. This work develops a realistic model that helps informed ...
Forecasting of time series of energy consumption in different regions OverviewDataDiscussionLeaderboardRules Leaderboard search The private leaderboard is calculated with approximately 50% of the test data. This competition has completed. This leaderboard reflects the final standings. ...
Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Learn more OK, Got it.АлексейРоманенко · Community Prediction Competition · 7 years ago Late Submission more_horiz Advanced Energy Consumption Forecast Forecasting of ti...
energy consumption forecasting; LSTM; NARX-MLP; model reliance; machine learning; time series prediction1. Introduction Currently, climate change and natural resource shortages have become significant issues. According to the research of Gaya Herrington [1], resources will run out in a few decades ...