Predicting energy demand peak using M5 model trees

Predicting energy demand peak is a key factor for reducing energy demand and electricity bills for commercial customers. Features influencing energy demand are many and complex, such as occupant behaviours and temperature. Feature selection can decrease prediction model complexity without sacrificing performance. In this paper, features were selected based on their multiple linear regression correlation coefficients. This paper discusses the capabilities of M5 model trees in energy demand prediction for commercial buildings. M5 model trees are similar to regression trees; however they are more suitable for continuous prediction problems. The M5 model tree prediction was developed based on a selected feature set including sensor energy demand readings, day of the week, season, humidity, and weather conditions (sunny, rain, etc.). The performance of the M5 model tree was evaluated by comparing it to the support vector regression (SVR) and artificial neural networks (ANN) models. The M5 model tree outperformed the SVR and ANN models with a mean absolute error (MAE) of 8.94 compared to 10.02 and 12.04 for the SVR and ANN models respectively

Same-day correction of baselines for demand response using long short-term memory

In incentive-based the Demand Response, the amount of electricity demand reduction is calculated by subtracting actual electricity demand from the baseline (BL). The BL is the estimated electricity demand of households when no electricity demand suppression is performed. In Japan, the high 4 of 5 method is used to forecast the BL by averaging the actual demand of the day. In this study, we refer to the high 4 of 5 method as BL1. BL2 is the BL to which the value of the same-day adjustment is added based on the actual demand of the day. BL3 is BL1 plus the value of the same-day adjustment predicted using Long Short-Term Memory (LSTM). The average MAE values for BL2 and BL3, calculated using actual electricity demand data from October 15, 2021, to December 24, 2021, were 11.2 kW and 8.1 kW, respectively, with BL3 being 3.1 kW smaller than BL2. To estimate the confidence intervals for BL2 and BL3, we calculated the error by subtracting each BL from the actual value and calculated the ±3σ equivalent for the distribution of the error. The confidence interval calculated for BL3 was found to be ±9.2 kW lower than that for BL2. The F-test for the distribution of the errors for BL2 and BL3 yielded a P-value of 4.05 × 10-50, indicating that the variances of the two distributions were not equally distributed

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Energy Consumption Prediction with Big Data: Balancing Prediction Accuracy and Computational Resources

In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is not a novel concept, it has great potential in the Big Data domain because it reduces computational complexity. The local SVR approach presented here is compared to traditional SVR and to deep neural networks with an H2O machine learning platform for Big Data. Local SVR outperformed both SVR and H2O deep learning in terms of prediction accuracy and computation time. Especially significant was the reduction in training time; local SVR training was an order of magnitude faster than SVR or H2O deep learning

An intelligent solar powered battery buffered EV charging station with solar electricity forecasting and EV charging load projection functions

An intelligent energy management approach for a solar-powered EV charging station with energy storage has been studied and demonstrated for a level 2 charger at the University of California-Davis West Village. The approach introduces solar PV electrical energy forecasting and EV charging demand projection to optimize the energy management of the charging station. The percentage of cloud cover is extracted from a weather forecast website for estimating the available PV electrical energy. A linear fit of the historical EV charging load from the same day of the week over the previous six weeks is employed for extracting the charging pattern of the workplace EV charging station. Both simulations and actual operation show that intelligent energy management for a charging station with a buffer battery can reduce impacts of the EV charging system on utility grids in terms of peak power demand and energy exchange, reduce grid system losses, and benefit the charging station owner through the Time-of-Use rate plans

An Overview of Electricity Demand Forecasting Techniques

Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. This paper presents a review of electricity demand forecasting techniques. The various types of methodologies and models are included in the literature. Load forecasting can be broadly divided into three categories: short-term forecasts which are usually from one hour to one week, medium forecasts which are usually from a week to a year, and long-term forecasts which are longer than a year.  Based on the various types of studies presented in these papers, the load forecasting techniques may be presented in three major groups: Traditional Forecasting technique, Modified Traditional Technique and Soft Computing Technique. Keywords: Electricity Demand, Forecasting Techniques, Soft Computing, Regression method, SVM