Daily electricity consumption forecasting is a classical problem. Existing
forecasting algorithms tend to have decreased accuracy on special dates like
holidays. This study decomposes the daily electricity consumption series into
three components: trend, seasonal, and residual, and constructs a two-stage
prediction method using piecewise linear regression as a filter and Dilated
Causal CNN as a predictor. The specific steps involve setting breakpoints on
the time axis and fitting the piecewise linear regression model with one-hot
encoded information such as month, weekday, and holidays. For the challenging
prediction of the Spring Festival, distance is introduced as a variable using a
third-degree polynomial form in the model. The residual sequence obtained in
the previous step is modeled using Dilated Causal CNN, and the final prediction
of daily electricity consumption is the sum of the two-stage predictions.
Experimental results demonstrate that this method achieves higher accuracy
compared to existing approaches.

日常电力消耗预测是一个经典问题，本研究通过使用分段线性回归和扩张因果卷积神经网络构建两阶段预测方法，将每日电力消耗系列拆分为趋势、季节和残差三个组件，并引入距离变量来挑战传统节日预测，最终预测结果通过两阶段预测的求和得出，实验结果表明该方法较现有方法具有更高的准确性。

基于分段线性回归和扩张因果卷积神经网络的中长期日电力消费预测

Mid-Long Term Daily Electricity Consumption Forecasting Based on  Piecewise Linear Regression and Dilated Causal CNN

In electricity markets, retailers or brokers want to maximize profits by
allocating tariff profiles to end consumers. One of the objectives of such
demand response management is to incentivize the consumers to adjust their
consumption so that the overall electricity procurement in the wholesale
markets is minimized, e.g. it is desirable that consumers consume less during
peak hours when cost of procurement for brokers from wholesale markets are
high. We consider a greedy solution to maximize the overall profit for brokers
by optimal tariff profile allocation. This in-turn requires forecasting
electricity consumption for each user for all tariff profiles. This forecasting
problem is challenging compared to standard forecasting problems due to
following reasons: i. the number of possible combinations of hourly tariffs is
high and retailers may not have considered all combinations in the past
resulting in a biased set of tariff profiles tried in the past, ii. the
profiles allocated in the past to each user is typically based on certain
policy. These reasons violate the standard i.i.d. assumptions, as there is a
need to evaluate new tariff profiles on existing customers and historical data
is biased by the policies used in the past for tariff allocation. In this work,
we consider several scenarios for forecasting and optimization under these
conditions. We leverage the underlying structure of how consumers respond to
variable tariff rates by comparing tariffs across hours and shifting loads, and
propose suitable inductive biases in the design of deep neural network based
architectures for forecasting under such scenarios. More specifically, we
leverage attention mechanisms and permutation equivariant networks that allow
desirable processing of tariff profiles to learn tariff representations that
are insensitive to the biases in the data and still representative of the task.

研究了用基于深度神经网络的注意机制和置换等变网络设计电力市场中消费者对电费变化的响应模型，并提出了一种预测电力消耗和最优定价的方案。

基于时段电价的用电量预测

Electricity Consumption Forecasting for Out-of-distribution Time-of-Use  Tariffs

Electricity consumption forecasting has important implications for the
mineral companies on guiding quarterly work, normal power system operation, and
the management. However, electricity consumption prediction for the mineral
company is different from traditional electricity load prediction since mineral
company electricity consumption can be affected by various factors (e.g., ore
grade, processing quantity of the crude ore, ball milling fill rate). The
problem is non-trivial due to three major challenges for traditional methods:
insufficient training data, high computational cost and low prediction
accu-racy. To tackle these challenges, we firstly propose a Regressive
Convolution Neural Network (RCNN) to predict the electricity consumption. While
RCNN still suffers from high computation overhead, we utilize RCNN to extract
features from the history data and Regressive Support Vector Machine (SVR)
trained with the features to predict the electricity consumption. The
experimental results show that the proposed RCNN-SVR model achieves higher
accuracy than using the traditional RNN or SVM alone. The MSE, MAPE, and
CV-RMSE of RCNN-SVR model are 0.8564, 1.975%, and 0.0687% respectively, which
illustrates the low predicting error rate of the proposed model.

本篇研究提出了一种基于 RCNN 和 SVR 的电力消费预测方法，通过提取历史数据的特征来预测矿业企业的电力消费，相比传统方法，该方法有更高的准确性，并降低了预测误差率。