One month-ahead electricity prices open up a great potential for manufacturing companies that aim at reducing energy costs. On the machine level, an energy cost-oriented scheduling can be realized through determining the least electricity cost-intensive launch times for job processing during a day. To obtain reliable price information at production planning stage in advance, well-performing forecast models are required. In this paper, we apply a Weighted Nearest Neighbor (WNN) approach, a TBATS approach, and a Deep Feedforward Neural Network (DFNN) approach to forecast EPEX Spot day-ahead electricity prices of the European Energy Exchange in Germany/Austria, while accounting for a forecast horizon of up to one month. Model performances are compared by means of established error measures as well as by adjusted error measures to better interpret results from a production planner's point of view. For evaluation purposes, 1357 different forecasts were computed for each of the methods, allowing valid conclusions as to their performance. The results show that all approaches lead to acceptable accuracies. However, the DFNN outperforms WNN and TBATS, providing well-fitting forecasts even 29 days ahead. Display omitted •Artificial Neural Networks outperform TBATS and Weighted Nearest Neighbor.•Forecast results are highly applicable for mid-term production planning purposes.•Neural Network forecasts for longer terms are almost as precise as for shorter ones.•On average, only 5% of the adjusted forecasts exceed the limit of a 10.00 EUR error.